API & Measurements on Swabian Instruments

Analyzing Timestamps from Other Time Tagger Devices

Tue, 20 Aug 2024 00:00:00 +0000

Our software is specifically designed to work with Swabian Instruments’ Time Tagger products. The Time Tagger Virtual, for instance, uses ttbin files as its data source. These time-tag dump files are generated exclusively by our Time Tagger hardware.

We dedicate significant effort to optimizing the performance of our measurement classes, ensuring exceptional speed and efficiency. This unique level of optimization sets our products apart from the competition. As a result, our software is not compatible with third-party Time Tagger devices, and we do not provide access to the corresponding source code.

Choosing an Appropriate Bin Width for Histograms

Tue, 20 Aug 2024 00:00:00 +0000

With our Time Tagger you can choose any binwidth in the range from 1 ps to more than a day. The entire range is adjustable with one picosecond resolution. In addition to the number of bins, this setting determines the maximum time difference that you measure. This flexibility allows you to choose a proper binwidth purely based on the requirements of your experiment.

The following questions may help you identify and decide on the optimal bin width value for your measurement:

Exporting Data and Saving Plots

Tue, 20 Aug 2024 00:00:00 +0000

1. Do you need to save raw data into a text file, for instance, to perform custom analyses?

If so, you need to use the measurement class TimeTagStream, get the data using getData() and save them, for instance, using savetxt from numpy library (numpy.savetxt), similarly to what shown in Question 3. Alternatively, you can use the measurement class FileWriter to store the raw data into a ttbin file. To save time tags as text from these files, please see the attached Python code at the end of the document.

Maximum Time Span Window for Measurements

Tue, 20 Aug 2024 00:00:00 +0000

Time tags are represented as 64-bit integers in picoseconds; therefore the maximum time window of a measurement is approximately 107 days in both directions. However, the measurement accuracy for larger time intervals requires a correspondingly accurate as well as stable clock signal.

The internal clock of our Time Tagger 20 has a clock sufficiently stable to achieve jitter-limited measurements of time intervals up to about 100 ns, whereas with our Time Tagger Ultra and Time Tagger X, the internal clock is sufficiently stable to achieve measurements with a time span of up to 1-10 ms. Our Time Taggers can certainly measure larger time intervals, but users should expect increased time uncertainty beyond these referenced limits. For precise measurements over very long time intervals, users can introduce an external clock source with our Time Tagger Ultra or Time Tagger X.

Measuring the Average Count Rate

Tue, 20 Aug 2024 00:00:00 +0000

Time Tagger provides a Countrate measurement class that allows for the measurement of average count rate. The averaging can be done over infinite time (as long as measurement runs) and over precisely defined averaging duration.

Below is the Python code that shows how to perform average countrate measurement over infinite and well-defined durations.

Time Tagger connection


from time import sleep
import TimeTagger

# Create a TimeTagger instance to control your hardware
tagger = TimeTagger.createTimeTagger()

channels = [1, 2]

# This enables internal test signals for demo purposes.
# Comment the following line if you want to try this with an external signals
tagger.setTestSignal(channels, True)

Infinite average

Measure the Countrate over the whole run time:

Single vs. Multiple Start/Stop in Time Histograms

Tue, 20 Aug 2024 00:00:00 +0000

A measurement is of single start / single stop type when only time differences between each stop and the preceding start click are considered and accumulated into the histogram. On the other hand, multiple start / multiple stop analyses consider time differences between all start and all stop clicks, provided these differences fall within the measurement time span.

Time Histograms	Unidirectional (stop after start)	Bidirectional
Single start / single stop	StartStop, Histogram2D, HistogramND
Multiple start / multiple stop	Histogram, HistogramLogBins, TimeDifferences, TimeDifferencesND	Correlation
Single start / multiple stop	Hist2D_single_start_multiple_stop.py

Using the Same Channel in Multiple Measurements

Tue, 20 Aug 2024 00:00:00 +0000

Our API allows multiple measurement objects to access the same time tag stream simultaneously. Each measurement runs in its own thread, sharing the same event information. The number of measurements you can create is limited only by your CPU’s performance (clock speed, cores) and available memory.

For example, in our trade fair demonstrations, we successfully run about 10 simultaneous measurements on a Microsoft Surface tablet PC. Naturally, the required processing power also depends on the event rate on the physical channels.