PyDDM.ddm_calc.temporalVarianceDDMMatrix
PyDDM.ddm_calc.temporalVarianceDDMMatrix¶
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temporalVarianceDDMMatrix(imageArray, dt, use_BH_windowing=False, quiet=False, overlap_method=2, **kwargs)¶ Calculates DDM matrix as a function of time at given lag time
This function calculates the DDM matrix at a given lag time. Does not average over time. This allows you to see the spread in the DDM matrix. Inspired by the analysis done in: Gao, Y., Kim, J. & Helgeson, M. E. Microdynamics and arrest of coarsening during spinodal decomposition in thermoreversible colloidal gels. Soft Matter 11, 6360–6370 (2015)
- Parameters
imageArray (array) – 3D array of images. First dimension should be time.
dt (int) – lag time for which to calculate the DDM matrix (in unit of frames)
use_BH_windowing ({True, False}, optional) – Apply Blackman-Harris windowing to the images if True. Default is False.
overlap_method ({0,1,2}, optional) – Default is 1.
quiet ({True, False}, optional) – If True, prints updates as the computation proceeds
**number_differences_max (optional keyword argument) – For overlap_method of 1, sets the maximum number of differences to find for a given lag time. If `overlap_method`=1 and this keyword argument is not given, defaults to 300
- Returns
ddm_mat (array) – The DDM matrix for given lag time. First dimension is time. Other two are the x and y wavevectors.
radial_avg_ddm (array) – Radial average of ddm_mat