Correlation routines¶

tidynamics.
acf
(data)¶ Autocorrelation function (ACF) of the input data using the Fast Correlation Algorithm.
For Ddimensional time series, a sum is performed on the last dimension.
Parameters: data (arraylike) – The input signal, of shape (N,) or (N,D). Returns: ndarray of shape (N,) with the autocorrelation for successive linearly spaced time delays

tidynamics.
msd
(pos)¶ Meansquared displacement (MSD) of the input trajectory using the Fast Correlation Algorithm.
Parameters: pos (arraylike) – The input trajectory, of shape (N,) or (N,D). Returns: ndarray of shape (N,) with the MSD for successive linearly spaced time delays.

tidynamics.
cross_displacement
(pos)¶ Cross displacement of the components of the input trajectory.
Parameters: pos (arraylike) – The input trajectory, of shape (N, D). Returns: list of lists of times series, where the fist two indices [i][j] denote the coordinates for the cross displacement: “(Delta pos[:,i]) (Delta pos[:,j])”.

tidynamics.
correlation
(data1, data2)¶ Correlation between the input data using the Fast Correlation Algorithm.
For Ddimensional time series, a sum is performed on the last dimension.
Parameters:  data1 (arraylike) – The first input signal, of shape (N,) or (N,D).
 data2 (arraylike) – The first input signal, of equal shape as data1.
Returns: ndarray of shape (2*N1,) with the correlation for “data1*data2[tau]” where tau is the lag in units of the timestep in the input data. The correlation is given from time N to time N.