def dynamic_analizer(topfile, trajfile, topformat, trjformat, dist_file,
selection_atom):
# cargo la dinamica.
print('Loading Molecular Dynamic simulations....\n')
traj = Universe(topfile,
trajfile,
topology_format=topformat,
format=trjformat)
#X = np.empty(shape=[0, 3])
#creo una lista de residuos
residues = traj.select_atoms("all").residues.resids
#print(residues)
# Defino el numero de frames.
NumbOfFrames = len(traj.trajectory)
# Progress bar
homespinner(True)
#for ts in traj.trajectory:
# a=1
from MDAnalysis.analysis import contacts
q1q2 = contacts.q1q2(traj, 'name CA', radius=8)
q1q2.run()
f, ax = plt.subplots(1, 2, figsize=plt.figaspect(0.5))
ax[0].plot(q1q2.timeseries[:, 0], q1q2.timeseries[:, 1], label='q1')
ax[0].plot(q1q2.timeseries[:, 0], q1q2.timeseries[:, 2], label='q2')
ax[0].legend(loc='best')
ax[0].set(xlabel='Frame', ylabel='Fraction Q')
ax[1].plot(q1q2.timeseries[:, 1], q1q2.timeseries[:, 2], '.-')
ax[1].set(xlabel='Q1', ylabel='Q2', title='2D Native Contacts Analysis.')
f.savefig('q1q2.pdf')
#Finish Progress bar.
homespinner(False)
print(
"\n\nDone\n\nThe q1,q2 plots were saved in the current directory...\n")
def test_q1q2():
u = mda.Universe(PSF, DCD)
q1q2 = contacts.q1q2(u, 'name CA', radius=8)
q1q2.run()
q1_expected = [1., 0.98092643, 0.97366031, 0.97275204, 0.97002725,
0.97275204, 0.96276113, 0.96730245, 0.9582198, 0.96185286,
0.95367847, 0.96276113, 0.9582198, 0.95186194, 0.95367847,
0.95095368, 0.94187103, 0.95186194, 0.94277929, 0.94187103,
0.9373297, 0.93642144, 0.93097184, 0.93914623, 0.93278837,
0.93188011, 0.9373297, 0.93097184, 0.93188011, 0.92643052,
0.92824705, 0.92915531, 0.92643052, 0.92461399, 0.92279746,
0.92643052, 0.93278837, 0.93188011, 0.93369664, 0.9346049,
0.9373297, 0.94096276, 0.9400545, 0.93642144, 0.9373297,
0.9373297, 0.9400545, 0.93006358, 0.9400545, 0.93823797,
0.93914623, 0.93278837, 0.93097184, 0.93097184, 0.92733878,
0.92824705, 0.92279746, 0.92824705, 0.91825613, 0.92733878,
0.92643052, 0.92733878, 0.93278837, 0.92733878, 0.92824705,
0.93097184, 0.93278837, 0.93914623, 0.93097184, 0.9373297,
0.92915531, 0.93188011, 0.93551317, 0.94096276, 0.93642144,
0.93642144, 0.9346049, 0.93369664, 0.93369664, 0.93278837,
0.93006358, 0.93278837, 0.93006358, 0.9346049, 0.92824705,
0.93097184, 0.93006358, 0.93188011, 0.93278837, 0.93006358,
0.92915531, 0.92824705, 0.92733878, 0.92643052, 0.93188011,
0.93006358, 0.9346049, 0.93188011]
assert_array_almost_equal(q1q2.timeseries[:, 1], q1_expected)
q2_expected = [0.94649446, 0.94926199, 0.95295203, 0.95110701, 0.94833948,
0.95479705, 0.94926199, 0.9501845, 0.94926199, 0.95387454,
0.95202952, 0.95110701, 0.94649446, 0.94095941, 0.94649446,
0.9400369, 0.94464945, 0.95202952, 0.94741697, 0.94649446,
0.94188192, 0.94188192, 0.93911439, 0.94464945, 0.9400369,
0.94095941, 0.94372694, 0.93726937, 0.93819188, 0.93357934,
0.93726937, 0.93911439, 0.93911439, 0.93450185, 0.93357934,
0.93265683, 0.93911439, 0.94372694, 0.93911439, 0.94649446,
0.94833948, 0.95110701, 0.95110701, 0.95295203, 0.94926199,
0.95110701, 0.94926199, 0.94741697, 0.95202952, 0.95202952,
0.95202952, 0.94741697, 0.94741697, 0.94926199, 0.94280443,
0.94741697, 0.94833948, 0.94833948, 0.9400369, 0.94649446,
0.94741697, 0.94926199, 0.95295203, 0.94926199, 0.9501845,
0.95664207, 0.95756458, 0.96309963, 0.95756458, 0.96217712,
0.95756458, 0.96217712, 0.96586716, 0.96863469, 0.96494465,
0.97232472, 0.97140221, 0.9695572, 0.97416974, 0.9695572,
0.96217712, 0.96771218, 0.9704797, 0.96771218, 0.9695572,
0.97140221, 0.97601476, 0.97693727, 0.98154982, 0.98431734,
0.97601476, 0.9797048, 0.98154982, 0.98062731, 0.98431734,
0.98616236, 0.9898524, 1.]
assert_array_almost_equal(q1q2.timeseries[:, 2], q2_expected)
def test_q1q2():
u = mda.Universe(PSF, DCD)
q1q2 = contacts.q1q2(u, 'name CA', radius=8)
q1q2.run()
q1_expected = [1., 0.98092643, 0.97366031, 0.97275204, 0.97002725,
0.97275204, 0.96276113, 0.96730245, 0.9582198, 0.96185286,
0.95367847, 0.96276113, 0.9582198, 0.95186194, 0.95367847,
0.95095368, 0.94187103, 0.95186194, 0.94277929, 0.94187103,
0.9373297, 0.93642144, 0.93097184, 0.93914623, 0.93278837,
0.93188011, 0.9373297, 0.93097184, 0.93188011, 0.92643052,
0.92824705, 0.92915531, 0.92643052, 0.92461399, 0.92279746,
0.92643052, 0.93278837, 0.93188011, 0.93369664, 0.9346049,
0.9373297, 0.94096276, 0.9400545, 0.93642144, 0.9373297,
0.9373297, 0.9400545, 0.93006358, 0.9400545, 0.93823797,
0.93914623, 0.93278837, 0.93097184, 0.93097184, 0.92733878,
0.92824705, 0.92279746, 0.92824705, 0.91825613, 0.92733878,
0.92643052, 0.92733878, 0.93278837, 0.92733878, 0.92824705,
0.93097184, 0.93278837, 0.93914623, 0.93097184, 0.9373297,
0.92915531, 0.93188011, 0.93551317, 0.94096276, 0.93642144,
0.93642144, 0.9346049, 0.93369664, 0.93369664, 0.93278837,
0.93006358, 0.93278837, 0.93006358, 0.9346049, 0.92824705,
0.93097184, 0.93006358, 0.93188011, 0.93278837, 0.93006358,
0.92915531, 0.92824705, 0.92733878, 0.92643052, 0.93188011,
0.93006358, 0.9346049, 0.93188011]
assert_array_almost_equal(q1q2.timeseries[:, 1], q1_expected)
q2_expected = [0.94649446, 0.94926199, 0.95295203, 0.95110701, 0.94833948,
0.95479705, 0.94926199, 0.9501845, 0.94926199, 0.95387454,
0.95202952, 0.95110701, 0.94649446, 0.94095941, 0.94649446,
0.9400369, 0.94464945, 0.95202952, 0.94741697, 0.94649446,
0.94188192, 0.94188192, 0.93911439, 0.94464945, 0.9400369,
0.94095941, 0.94372694, 0.93726937, 0.93819188, 0.93357934,
0.93726937, 0.93911439, 0.93911439, 0.93450185, 0.93357934,
0.93265683, 0.93911439, 0.94372694, 0.93911439, 0.94649446,
0.94833948, 0.95110701, 0.95110701, 0.95295203, 0.94926199,
0.95110701, 0.94926199, 0.94741697, 0.95202952, 0.95202952,
0.95202952, 0.94741697, 0.94741697, 0.94926199, 0.94280443,
0.94741697, 0.94833948, 0.94833948, 0.9400369, 0.94649446,
0.94741697, 0.94926199, 0.95295203, 0.94926199, 0.9501845,
0.95664207, 0.95756458, 0.96309963, 0.95756458, 0.96217712,
0.95756458, 0.96217712, 0.96586716, 0.96863469, 0.96494465,
0.97232472, 0.97140221, 0.9695572, 0.97416974, 0.9695572,
0.96217712, 0.96771218, 0.9704797, 0.96771218, 0.9695572,
0.97140221, 0.97601476, 0.97693727, 0.98154982, 0.98431734,
0.97601476, 0.9797048, 0.98154982, 0.98062731, 0.98431734,
0.98616236, 0.9898524, 1.]
assert_array_almost_equal(q1q2.timeseries[:, 2], q2_expected)
# contact analysis
import MDAnalysis as mda
from MDAnalysis.analysis import contacts
u = mda.Universe("/home/bdv1/VMD_SaveFolder/PRB-0-HIE_ProteinOnly.pdb", "/home/bdv1/Desktop/PRB-0-protein-000.dcd")
eitrig = contacts.q1q2(u, 'segid AP1', radius=3)
eitrig.run()
eitrig.save('con_try_contacts.txt')
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import numpy
u = mda.Universe('/home/bdv1/Desktop/rpn11_ubq.psf',
'/home/bdv1/Desktop/rpn11_ubq.dcd')
sel_1 = "segid RN11"
sel_2 = "segid UBQ"
selection_1 = u.select_atoms(sel_1)
selection_2 = u.select_atoms(sel_2)
print("Printing selection_1")
print selection_1
print("Printing selection_2")
print selection_2
q1q2 = contacts.q1q2(u, 'segid RN11', radius=8)
q1q2.run()
f, ax = plt.subplots(1, 2, figsize=plt.figaspect(0.5))
ax[0].plot(q1q2.timeseries[:, 0], q1q2.timeseries[:, 1], label='q1')
ax[0].plot(q1q2.timeseries[:, 0], q1q2.timeseries[:, 2], label='q2')
ax[0].legend(loc='best')
ax[1].plot(q1q2.timeseries[:, 1], q1q2.timeseries[:, 2], '.-')
f.show()
plt.pause(20)
import MDAnalysis as mda
from MDAnalysis.analysis import contacts
from MDAnalysisTests.datafiles import PSF, DCD
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
u = mda.Universe(PSF, DCD)
#u = mda.Universe("/home/bdv1/Desktop/rpn11_ubq.psf", "/home/bdv1/Desktop/rpn11_ubq.dcd")
q1q2 = contacts.q1q2(u, 'name CA', radius=8)
q1q2.run()
plt.ion()
f, ax = plt.subplots(1, 2, figsize=plt.figaspect(0.5))
ax[0].plot(q1q2.timeseries[:, 0], q1q2.timeseries[:, 1], label='q1')
ax[0].plot(q1q2.timeseries[:, 0], q1q2.timeseries[:, 2], label='q2')
ax[0].legend(loc='best')
ax[1].plot(q1q2.timeseries[:, 1], q1q2.timeseries[:, 2], '.-')
f.show()
plt.pause(20)