Peter-Barrow/tangy

tangy is a high performance timetag analysing library, providing fast buffering of data and soft-realtime analysis

Welcome to tangy 🍊

tangy is a high performance library to buffer timetags from timetaggers and files and provides soft-realtime analysis.

About

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.

Features

• Support for different timetag formats
• A client-server model for buffering and analysis
• Analysis for:
  • Singles counting
  • Coincidence counting
  • Delay finding
  • Joint delay histograms

Installation

python3 -m pip install tangy
python3 -m pip install tangy[gui] # if you intend on using the guis

Advanced

Install from git to get the latest version

python3 -m pip install git+https://gitlab.com/Peter-Barrow/tangy.git

Quick Examples

Open 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)