rd.td_parameters()#
- RotationalDiffusion.td_analysis.td_parameters(lag_times, Q_data)[source]#
Extract time-dependent rotational diffusion parameters from rotational correlation functions.
- Parameters:
- lag_times(N,) array_like
Discrete lag times at which the rotational correlation functions were computed.
- Q_data(…, N, 3, 3) array_like
Symmetric quaternion covariance matrices containing six rotational correlation functions. The matrix elements are stored along the last two dimensions.
- Returns:
- D_time_dep(…, N, 3)
ndarray The time-dependent diffusion coefficients in the principal coordinate system (PCS) in increasing order. The units are inverse time, matching the unit of the input
lag_times.- PCS_time_dep(…, N, 3, 3)
ndarray The corresponding time-dependent principal coordinate system. The row vectors of
PCS_time_depare the time-dependent principal axes. By convention, the PCS is right-handed and the \(xx\) and \(yy\) coordinates are positive.
- D_time_dep(…, N, 3)
Notes
Theoretical Background
First, the time-dependent principal component system is found by solving the matrix-eigenvalue equation of
Q_dataat each lag time. Then, the eigenvalues are used to compute the time-dependent diffusion coefficients.