def test_asa_3():
traj_ref = np.loadtxt( os.path.join(reference_dir(),'g_sas_ref.dat'))
Conf = Trajectory.load_from_pdb(os.path.join( fixtures_dir(), 'native.pdb'))
traj = Trajectory.load_trajectory_file( os.path.join(fixtures_dir(), 'trj0.xtc') , Conf=Conf)
traj_asa = calculate_asa(traj, probe_radius=0.14, n_sphere_points = 960)
# the algorithm used by gromacs' g_sas is slightly different than the one
# used here, so the results are not exactly the same -- see the comments
# in src/python/geomtry/asa.py or the readme file src/ext/asa/README.txt
# for details
npt.assert_array_almost_equal(traj_asa, traj_ref, decimal=2)
def setUp(self):
test_dir = os.path.join( reference_dir(), 'cfep_reference/' )
self.generators = Trajectory.load_trajectory_file(test_dir + 'Gens.lh5')
N = len(self.generators)
self.counts = io.mmread(test_dir + 'tCounts.mtx')
self.lag_time = 1.0
self.pfolds = np.random.rand(N)
self.rescale = False
self.reactant = 0
self.product = N
def test_FahProjectBuilder():
cd = os.getcwd()
td = tempfile.mkdtemp()
os.chdir(td)
traj_dir = os.path.join(reference_dir(),
"project_reference/project.builder/fah_style_data")
conf_filename = os.path.join(traj_dir, 'native.pdb')
pb = FahProjectBuilder(traj_dir, '.xtc', conf_filename)
project = pb.get_project()
eq_(project.n_trajs, 4)
npt.assert_array_equal(project.traj_lengths, [1001, 1001, 501, 1001])
os.chdir(cd)
shutil.rmtree(td)
def setUp(self):
test_dir = os.path.join(reference_dir(), 'cfep_reference/')
self.generators = Trajectory.load_trajectory_file(test_dir +
'Gens.lh5')
N = len(self.generators)
self.counts = io.mmread(test_dir + 'tCounts.mtx')
self.lag_time = 1.0
self.pfolds = np.random.rand(N)
self.rescale = False
self.reactant = 0
self.product = N
def setUp(self):
# load in the reference data
self.tpt_ref_dir = os.path.join(reference_dir(), "transition_path_theory_reference")
self.tprob = scipy.io.mmread( os.path.join(self.tpt_ref_dir, "tProb.mtx") ) #.toarray()
self.sources = [0] # chosen arbitarily by TJL
self.sinks = [70] # chosen arbitarily by TJL
self.waypoints = [60] # chosen arbitarily by TJL
self.lag_time = 1.0 # chosen arbitarily by TJL
# set up the reference data for hub scores
self.hub_ref_dir = os.path.join(self.tpt_ref_dir, "hub_ref")
K = np.loadtxt( os.path.join(self.hub_ref_dir, 'ratemat_1.dat') )
#self.hub_T = scipy.linalg.expm( K ) # delta-t should not affect hub scores
self.hub_T = np.transpose( np.genfromtxt(os.path.join(self.hub_ref_dir, 'mat_1.dat'))[:,:-3] )
for i in range(self.hub_T.shape[0]):
self.hub_T[i,:] /= np.sum(self.hub_T[i,:])
self.hc = np.loadtxt( os.path.join(self.hub_ref_dir, 'fraction_visited.dat') )
self.Hc = np.loadtxt( os.path.join(self.hub_ref_dir, 'hub_scores.dat') )[:,2]
