antibunching

Module for performing antibunching-type calculations.

Currently only performs simple second-order correlation. More advanced functionality might be added in the future.

Bertus van Heerden, University of Pretoria, 2023

Module Contents

Classes

AntibunchingAnalysis

Performs second-order correlation and stores the result.

Attributes

__docformat__
logger

antibunching.__docformat__ = "'NumPy'"

antibunching.logger = "'antibunching'"

class antibunching.AntibunchingAnalysis(particle)

Performs second-order correlation and stores the result.

Parameters:

particlesmsh5.particle

particle object to perform analysis on.

_particle

uuid

corr_hist = 'None'

corr_bins = 'None'

corr_events = 'None'

has_corr = 'False'

correlate_particle(difftime: float, window: float, binsize: float)

Calculate second-order correlation for this particle.

Mainly a wrapper around static method correlate_times. Stores result in class attributes corr_bins, corr_hist and corr_events and sets has_corr to True.

Arguments:

difftimefloat

time difference between channels (ch. 1 - ch. 2) in ns

windowfloat

time window for correlation in ns

binsizefloat

bin size for correlation histogram in ns

static correlate_times(abstimes1, abstimes2, microtimes1, microtimes2, difftime=0.0, window=500.0, binsize=0.5)

Calculate second-order correlation based on time-tagged time-resolved photon data.

The function is a simple nested loop that runs through every photon within a certain window and checks for coincidences. Each coincidence gets a relative time, which are all put in a histogram to get the second-order correlation. Before the calculation, the arrival times are corrected based on the difftime parameter, which accounts for possible delay between two TCSPC cards.

Arguments:

abstimes11D array

absolute times for channel 1 in ns

abstimes21D array

absolute times for channel 2 in ns

microtimes11D array

micro times for channel 1 in ns

microtimes21D array

micro times for channel 2 in ns

difftimefloat

time difference between channels (ch. 1 - ch. 2) in ns

windowfloat

time window for correlation in ns

binsizefloat

bin size for correlation histogram in ns

Returns:

bins1D array

correlation histogram bins

corr1D array

correlation histogram values

events1D array

difftimes used to construct histogram, returned in case rebinning is needed.

rebin_corr(window, binsize)