12.1.1. Streamplots (2D) — MDAnalysis.visualization.streamlines


Tyler Reddy and Matthieu Chavent




GNU Public License v3

The generate_streamlines() function can generate a 2D flow field from a MD trajectory, for instance, lipid molecules in a flat membrane. It can make use of multiple cores to perform the analyis in parallel (using multiprocessing).

See also


streamplots in 3D

MDAnalysis.visualization.streamlines.generate_streamlines(topology_file_path, trajectory_file_path, grid_spacing, MDA_selection, start_frame, end_frame, xmin, xmax, ymin, ymax, maximum_delta_magnitude, num_cores='maximum')[source]

Produce the x and y components of a 2D streamplot data set.

  • topology_file_path (str) – Absolute path to the topology file

  • trajectory_file_path (str) – Absolute path to the trajectory file. It will normally be desirable to filter the trajectory with a tool such as GROMACS g_filter (see [ᵃChavent2014])

  • grid_spacing (float) – The spacing between grid lines (angstroms)

  • MDA_selection (str) – MDAnalysis selection string

  • start_frame (int) – First frame number to parse

  • end_frame (int) – Last frame number to parse

  • xmin (float) – Minimum coordinate boundary for x-axis (angstroms)

  • xmax (float) – Maximum coordinate boundary for x-axis (angstroms)

  • ymin (float) – Minimum coordinate boundary for y-axis (angstroms)

  • ymax (float) – Maximum coordinate boundary for y-axis (angstroms)

  • maximum_delta_magnitude (float) – Absolute value of the largest displacement tolerated for the centroid of a group of particles ( angstroms). Values above this displacement will not count in the streamplot (treated as excessively large displacements crossing the periodic boundary)

  • num_cores (int or 'maximum' (optional)) – The number of cores to use. (Default ‘maximum’ uses all available cores)


  • dx_array (array of floats) – An array object containing the displacements in the x direction

  • dy_array (array of floats) – An array object containing the displacements in the y direction

  • average_displacement (float) – \(\frac{\sum\sqrt[]{dx^2 + dy^2}}{N}\)

  • standard_deviation_of_displacement (float) – standard deviation of \(\sqrt[]{dx^2 + dy^2}\)


Generate 2D streamlines and plot:

import matplotlib, matplotlib.pyplot, np
import MDAnalysis, MDAnalysis.visualization.streamlines

u1, v1, average_displacement, standard_deviation_of_displacement =
    MDAnalysis.visualization.streamlines.generate_streamlines('testing.gro', 'testing_filtered.xtc',
            grid_spacing=20, MDA_selection='name PO4', start_frame=2, end_frame=3,
            xmin=-8.73000049591, xmax= 1225.96008301,
            ymin= -12.5799999237, ymax=1224.34008789,
            maximum_delta_magnitude=1.0, num_cores=16)
x = np.linspace(0, 1200, 61)
y = np.linspace(0, 1200, 61)
speed = np.sqrt(u1*u1 + v1*v1)
fig = matplotlib.pyplot.figure()
ax = fig.add_subplot(111, aspect='equal')
ax.set_xlabel('x ($\AA$)')
ax.set_ylabel('y ($\AA$)')
ax.streamplot(x, y, u1, v1, density=(10,10), color=speed, linewidth=3*speed/speed.max())



Matthieu Chavent, Tyler Reddy, Joseph Goose, Anna Caroline E. Dahl, John E. Stone, Bruno Jobard, and Mark S. P. Sansom. Methodologies for the analysis of instantaneous lipid diffusion in md simulations of large membrane systems. Faraday Discuss., 169:455–475, 2014. doi:10.1039/C3FD00145H.