Visualization¶
This page provides examples of how to visualize membrane curvature via Matplotlib. Two different approaches are suggested.
1. imshow¶
A simple plot using imshow() can be obtained like so:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.imshow(avg_mean_curvature, cmap='bwr', interpolation='gaussian', origin='lower')
ax.set_title('Mean Curvature')
plt.show()
With imshow(), each element of the array is plotted as a square in a matrix
of m x n elements. The color of each square is determined by the value of
the corresponding array element and the colormap of preference.
For example, to visualize the results obtained in 1. Membrane-only systems, we can run:
In [1]: import MDAnalysis as mda
...: from membrane_curvature.base import MembraneCurvature
...: from MDAnalysis.tests.datafiles import Martini_membrane_gro
...: import matplotlib.pyplot as plt
...:
In [2]: universe = mda.Universe(Martini_membrane_gro)
In [3]: curvature_upper_leaflet = MembraneCurvature(universe,
...: select='resid 1-225 and name PO4',
...: n_x_bins=8,
...: n_y_bins=8,
...: wrap=True).run()
...:
In [4]: mean_upper_leaflet = curvature_upper_leaflet.results.average_mean
In [5]: curvature_lower_leaflet = MembraneCurvature(universe,
...: select='resid 226-450 and name PO4',
...: n_x_bins=8,
...: n_y_bins=8,
...: wrap=True).run()
...:
In [6]: mean_lower_leaflet = curvature_lower_leaflet.results.average_mean
In [7]: leaflets = ['Lower', 'Upper']
In [8]: curvatures = [mean_lower_leaflet, mean_upper_leaflet]
In [9]: fig, [ax1, ax2] = plt.subplots(ncols=2, figsize=(6,3), dpi=200)
...: for ax, mc, lf in zip((ax1, ax2), curvatures, leaflets):
...: ax.imshow(mc, interpolation='gaussian', cmap='seismic', origin='lower')
...: ax.set_aspect('equal')
...: ax.set_title('{} Leaflet'.format(lf))
...: ax.axis('off')
...:
2. contourf¶
You can use contour plots using contourf(). With this
approach, contour lines and filled contours of the obtained two-dimensional data
are plotted. A contour line connects points with the same curvature values.
When plotting using contourf(), an extra step is required
to perform an interpolation. We suggest using
scipy.ndimage.gaussian_filter() as in:
In [10]: from scipy import ndimage
In [11]: leaflets = ['Lower', 'Upper']
In [12]: fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(5,3))
....: for ax, mc, lf in zip((ax1, ax2), curvatures, leaflets):
....: ax.contourf(ndimage.gaussian_filter(mc, sigma=1, order=0, mode='reflect'),
....: cmap='bwr',
....: levels=30)
....: ax.set_aspect('equal')
....: ax.set_title('{} Leaflet'.format(lf))
....: ax.axis('off')
....:
