def test_fit_noisy(self):
noise = np.sin(self.x) * 0.01
y2 = noise + self.y
a = polymer.fit_exponential_decay(self.x, y2)
assert_almost_equal(a, self.a_ref, decimal=3)
def test_fit_noisy(self):
noise = np.sin(self.x) * 0.01
y2 = noise + self.y
a = polymer.fit_exponential_decay(self.x, y2)
assert_almost_equal(a, self.a_ref, decimal=3)
def test_fit_simple(self):
a = polymer.fit_exponential_decay(self.x, self.y)
assert a == self.a_ref
def test_fit_noisy(self):
y2 = self.y + (np.random.random(len(self.y)) - 0.5) * 0.05
a = polymer.fit_exponential_decay(self.x, y2)
assert_(np.rint(a) == self.a_ref)
def test_fit_simple(self):
a = polymer.fit_exponential_decay(self.x, self.y)
assert_(a == self.a_ref)
def PersistenceL(trajFile,
top,
backboneAtoms,
NP,
coef=1. / 10.,
stride=1.,
chain0Id=0,
firstpoint=2):
"""
get persistence length of chains of indices from chain0Id to chain0Id+NP-1
coef: divide results by 10. to convert to nm since MDAnalysis uses Angstrom
"""
from MDAnalysis.lib.mdamath import make_whole
import MDAnalysis as mda
from MDAnalysis.analysis import polymer
u = mda.Universe(top, trajFile)
for frag in u.atoms.fragments:
make_whole(frag)
molecules = u.atoms.fragments
filter = 'name {}'.format(*backboneAtoms)
backbones = [mol.select_atoms(filter)
for mol in molecules][chain0Id:chain0Id + NP]
sorted_backbones = [polymer.sort_backbone(bb) for bb in backbones]
persistence_length = polymer.PersistenceLength(sorted_backbones)
persistence_length.run()
LB = persistence_length.lb * coef
if firstpoint == 0:
LP = persistence_length.lp * coef
else:
LP = polymer.fit_exponential_decay(
persistence_length.x[firstpoint:],
persistence_length.results[firstpoint:])
persistence_length.fit = np.exp(-persistence_length.x[firstpoint:] /
LP)
LP *= coef
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=[3, 2])
ax.plot(persistence_length.x * coef,
persistence_length.results,
'ro',
label='Result')
ax.plot(persistence_length.x[firstpoint:] * coef,
persistence_length.fit,
label='Fit')
ax.vlines(LP, 0., 1, ls=':', lw=0.75)
plt.text(2. * LP,
0.5 * max(persistence_length.fit),
'$<cos\\theta> = exp(-n*{}/{})$'.format(round(LB, 3),
round(LP, 3)),
size=7)
ax.set_xlim(-0.1, 1.1 * max(persistence_length.x * coef))
ax.set_xlabel(r'$n*l_b$')
ax.set_ylabel(r'$C(n*l_b)$')
plt.savefig('persistenceL.png',
dpi=500,
transparent=True,
bbox_inches="tight")
return LP, LB, len(sorted_backbones[0])
def test_fit_noisy(self):
y2 = self.y + (np.random.random(len(self.y)) - 0.5) * 0.05
a = polymer.fit_exponential_decay(self.x, y2)
assert_(np.rint(a) == self.a_ref)