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9.1.2. 3D Streamplot

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9.1.1. 2D Streamplot

9.1.1. Multicore 2D streamplot Python library for MDAnalysis — MDAnalysis.visualization.streamlines

Authors:Tyler Reddy and Matthieu Chavent
Year:2014
Copyright:GNU Public License v3
Citation:[Chavent2014]
MDAnalysis.visualization.streamlines.generate_streamlines(coordinate_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.

Parameters:
coordinate_file_path : str

Absolute path to the coordinate 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 [Chavent2014])

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, optional

The number of cores to use. (Default ‘maximum’ uses all available cores)

Returns:
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 \(\sqrt[]{dx^2 + dy^2}\)

Examples:
import matplotlib, matplotlib.pyplot, numpy
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 = numpy.linspace(0,1200,61)
y = numpy.linspace(0,1200,61)
speed = numpy.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())
fig.savefig('testing_streamline.png',dpi=300)
../../_images/testing_streamline.png
[Chavent2014](1, 2) Chavent, M.*, Reddy, T.*, Dahl, C.E., Goose, J., Jobard, B., and Sansom, M.S.P. (2014)

Methodologies for the analysis of instantaneous lipid diffusion in MD simulations of large membrane systems. Faraday Discussions 169: Accepted