def setUp(self):
self.u = MDAnalysis.Universe(PDB, XTC)
self.dist = diffusionmap.DistanceMatrix(self.u, select='backbone')
self.dmap = diffusionmap.DiffusionMap(self.dist)
self.dmap.run()
self.eigvals = self.dmap.eigenvalues
self.eigvects = self.dmap._eigenvectors
def test_dist_weights(u):
backbone = u.select_atoms('backbone')
weights_atoms = np.ones(len(backbone.atoms))
dist = diffusionmap.DistanceMatrix(u,
select='backbone',
weights=weights_atoms)
dist.run(step=3)
dmap = diffusionmap.DiffusionMap(dist)
dmap.run()
assert_array_almost_equal(dmap.eigenvalues, [1, 1, 1, 1], 4)
assert_array_almost_equal(dmap._eigenvectors,
([[0, 0, 1, 0], [0, 0, 0, 1],
[-.707, -.707, 0, 0], [.707, -.707, 0, 0]]), 2)
import MDAnalysis as mda
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from MDAnalysis.analysis import diffusionmap, align
# load in trajectorty data
U = mda.Universe(sys.argv[1], sys.argv[2])
# align trajectory based on CA
align_rms = align.AlignTraj(U, U, select='name CA', in_memory=True).run()
# create RMSD matrix for pairwise frames
matrix = diffusionmap.DistanceMatrix(U, select='name CA').run()
matrix.dist_matrix.shape
# plot
fig = plt.figure(figsize=(4, 4))
ax = fig.add_subplot(111)
im = plt.imshow(matrix.dist_matrix, origin='lower', cmap='rainbow')
#plt.colorbar(im, cmap='rainbow', ax=ax, label='RMSD ($\AA$)', pad=0.2, size=)
plt.xticks(np.arange(0, len(U.trajectory) + 1, step=(len(U.trajectory) / 5)))
plt.yticks(np.arange(0, len(U.trajectory) + 1, step=(len(U.trajectory) / 5)))
plt.xlabel('frame')
plt.ylabel('frame')
divider = make_axes_locatable(ax)
cb_ax = divider.append_axes("right", size="5%", pad=0.2)
plt.colorbar(im, cmap='rainbow', ax=ax, label='RMSD ($\AA$)', cax=cb_ax)
plt.savefig('2D-rmsd.png', bbox_inches='tight', dpi=900)
def dist(u):
return diffusionmap.DistanceMatrix(u, select='backbone')