multipletau_cor_tttr.correlate Reference

multipletau_cor_tttr.correlate.CCF(t1, t2, nblock=10, nc=10, nb='auto')

Performs crosscorrelation of time-tagged photon data t1 and t2 using semi-logarithmic timeaxis with nb logarithmic levels and nc equally spaced timebins per level. Error estimation is performed by splitting the measurement into nblock time segments of equal length and taking the standard error of mean. The returned array yields the correlation of intensity fluctuations, decaying to zero.

Parameters:

• t1: Numpy arrays of photon arrival times in channel 1 (integer type)
• t2: Numpy arrays of photon arrival times in channel 2 (integer type)
• nblock: Number of blocks used for error estimation. (Default: 10)
• nc: Number of time points per logarithmic level. (Default: 10)
• nb: Number of logarithmic levels. ‘auto’ takes the maximum possible lagtime to calculate nb.

Return:

• mcorr: 1d array of correlation result
• stdcorr: Standard error of mean of correlation result
• timeaxis: Timeaxis

multipletau_cor_tttr.correlate._CCF_inC(t1, t2, nc, nb, timeaxis)

Wrapper function to communicate between python and C using ctypes library. The returned array yields the correlation of intensity fluctuations, decaying to zero.

Parameters:

• t1: Numpy arrays of photon arrival times in channel 1 (integer type)
• t2: Numpy arrays of photon arrival times in channel 2 (integer type)
• nc: Number of time points per logarithmic step
• nb: Number of logarithmic steps
• timeaxis: Logarithmic timeaxis as defined by nc and nb

Return:

• corr_res: 1d array of correlation result