tangy is a high performance library to buffer timetags from timetaggers and files and provides soft-realtime analysis.
It stores your timetag data in a circular buffer backed by shared memory allowing you to have multiple client connect to the same buffer. When streaming data from a device into a tangy buffer this allows you to have multiple connections to the same device facilitating either mulitple lab users or multiple concurrent experiments. Alternatively, if you have a large file of containing timetags you can read a section into a tangy buffer in one python interpreter and perform analysis on that section in another speeding up exploratory analysis.
- Support for different timetag formats
- A client-server model for buffering and analysis
- Analysis for:
- Singles counting
- Coincidence counting
- Delay finding
- Joint delay histograms
python3 -m pip install tangy
python3 -m pip install tangy[gui] # if you intend on using the guisInstall from git to get the latest version
python3 -m pip install git+https://gitlab.com/PeterBarrow/tangy.gitOpen a file and read some data
import tangy
target_file = 'tttr_data.ptu'
n = int(1e7)
name = "tagbuffer"
# Open the file
ptu = tangy.PTUFile(target_file, name, n)
# Read some data from the file
for i in range(11):
start_time = perf_counter()
a = ptu.read(1e6)
stop_time = perf_counter()
run_time += (stop_time - start_time)
print([ptu.record_count, ptu.count])
# Acquire the buffer
buffer = ptu.buffer()Count coincidences in the last second for channels [0, 1] with a 1ns window
integration_time = 1
coincidence_window = 1e-9
channels = [0, 1]
count = buffer.coincidence_count(integration_time, coincidence_window, channels)Collect coincident timetags
records = buffer.coincidence_collect(integration_time, coincidence_window, channels)Find the delays between pairs of channels
channel_a = 0
channel_b = 1
integration_time = 10
measurement_resolution = 6.25e-9
result_delay = buffer.relative_delay(channel_a, channel_b,
integration_time,
resolution=6.25e-9,
window=250e-7)
delays = [0, result_delay.t0]Count (or collect) coincidences with delays
count = buffer.coincidence_count(integration_time,
coincidence_window,
channels,
delays=delays)
records = buffer.coincidence_collect(integration_time,
coincidence_window,
channels,
delays=delays)
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