ctrappy.diffusion

Calculate the diffusion coefficient from a track.

ctrappy.diffusion.fit_diffusion(msd, frame_time, p_min, diff_estimate, sigma, p_maxed=False)

Fit the msd with a linear function; take p_min points into account.

ctrappy.diffusion.fit_func(x, a, b)

Linear fitting function.

ctrappy.diffusion.get_diffusion_coefficient(x_vals, diff_estimate, t_exposure, avg_photon_count, sigma_psf, frame_time, show=False)

Get diffusion coefficient using p_min values; find p_min using a convergence scheme. Based on https://doi.org/10.1103/PhysRevE.82.041914.

Parameters:

  • x_vals (array of floats) – Positions of particle over time.

  • diff_estimate (float) – Diffusion coefficient estimate.

  • t_exposure (float) – Exposure time of spot.

  • avg_photon_count (float) – Average photon count for spot.

  • sigma_psf (float) – Sigma of psf.

  • frame_time (float) – Exposure time of frame.

  • show (bool (optional)) – Plot MSD if calculation does not converge. Default: False.

Returns:

diff – Diffusion coefficient.

Return type:

float

ctrappy.diffusion.get_msd(x_vals)

Get mean square displacement from an array of values.

ctrappy.diffusion.get_p_min(diff_estimate, t_exposure, avg_photon_count, sigma_psf, frame_time)

Calculate p_min from diffusion estimate, exposure time, average photon count, sigma of psf and frame time.