def test_init(self):
"""
test if the trajectory initialization is working properly:
- initializing the trajectory with an array, a tuple or from file must
give the same result
- test if compute_all = True runs without error
"""
traj = np.zeros((370, 4))
path = os.environ['TRAVIS_BUILD_DIR']
init_from_file = tj.Trajectory(path + '/data/samples/sample.csv',
skip_header=1,
delimiter=',')
traj[:, 0] = np.copy(init_from_file._t)
traj[:, 1:] = np.copy(init_from_file._r)
t = np.copy(traj[:, 0])
x, y, z = np.copy(traj[:, 1]), np.copy(traj[:, 2]), np.copy(traj[:, 3])
init_from_array = tj.Trajectory(traj)
init_from_tuple = tj.Trajectory((t, x, y, z))
self.assertEqual(init_from_array._r.all(), init_from_file._r.all())
self.assertEqual(init_from_tuple._r.all(), init_from_file._r.all())
self.assertRaises(TypeError, tj.Trajectory, 1.0)
r = tj.Trajectory(traj)
r.compute_features
def test_gyration_radius_and_asymmetry(self):
"""
testing the radius of gyration for some basic cases
circle: Rg = R/2
"""
length = 20
circle = np.zeros((length, 2))
radius = 10
circle_gyr = np.array([[radius, 0.], [0., radius]]) / 2
x1 = np.linspace(0, 100, 1000)
x2 = np.cos(np.pi * np.linspace(0, 100, 1000))
x1x2 = np.array([x1, x2]).transpose()
for n in range(0, length):
circle[n] = radius * np.array(
[np.cos(np.pi * n / 10),
np.sin(np.pi * n / 10)])
r = tj.Trajectory()
gyration_radius_circle = r.gyration_radius_(circle)
gyration_radius_osci = r.gyration_radius_(x1x2)
eigenvalues_circle = np.linalg.eigvals(gyration_radius_circle)
eigenvalues_osci = np.linalg.eigvals(gyration_radius_osci)
asymmetry_oscillatory = r.asymmetry_(eigenvalues_osci)
asymmetry_circle = r.asymmetry_(eigenvalues_circle)
self.assertAlmostEqual(asymmetry_oscillatory,
asymmetry_oscillatory,
places=1)
self.assertAlmostEqual(asymmetry_circle, asymmetry_circle, places=1)
self.assertEqual(
np.round(gyration_radius_circle, 2).all(),
np.round(circle_gyr, 2).all())
def test_fractal_dimension(self):
"""
test if the fractal dimension is being computed correctly
"""
x1 = np.linspace(0, 100, 100)
x2 = np.random.rand(100)
r = tj.Trajectory()
self.assertAlmostEqual(r.fractal_dimension_(x1)[0], 1.0, places=2)
self.assertGreaterEqual(r.fractal_dimension_(x2)[0], 3.0)
def test_green_kubo(self):
"""
test Green-Kubo diffusivity function
"""
r = tj.Trajectory()
x1 = np.linspace(0, 100, 100)
x2 = np.random.rand(100)
x3 = np.random.rand(100)
r._r = np.array([x1, x2, x3]).transpose()
r._t = np.linspace(0, 100, 100)
self.assertAlmostEqual(r.green_kubo_(), 50.53, places=1)
def test_kurtosis(self):
"""
testing the kurtosis function
"""
r = tj.Trajectory()
x1 = np.random.rand(100)
x2 = np.random.rand(100)
x3 = np.random.rand(100)
x1x2 = np.array([x1, x2, x3]).transpose()
r.gyration_radius = r.gyration_radius_(x1x2)
r.eigenvalues, r.eigenvectors = np.linalg.eig(r.gyration_radius)
idx = r.eigenvalues.argsort()[::-1]
r.kurtosis = r.kurtosis_(x1x2, r.eigenvectors[idx[0]])
self.assertAlmostEqual(r.kurtosis, 2.26, places=1)
def test_straightness(self):
"""
testing the straightness function for three cases
x1 - linear; x2 - oscillatory; x1x2 - mixed components
"""
x1 = np.linspace(0, 100, 1000)
x2 = np.cos(np.pi * np.linspace(0, 100, 1000))
x1x2 = np.array([x1, x2]).transpose()
r = tj.Trajectory()
self.assertAlmostEqual(r.straightness_(x1), 1.0, places=2)
self.assertAlmostEqual(r.straightness_(x2), 0.0, places=2)
self.assertAlmostEqual(r.straightness_(x1x2), 0.43, places=1)
def test_efficiency(self):
"""
test if the efficiency is being computed correctly for three cases
1) linear trajectory 2) random steps and 3) oscillatory.
"""
x1 = np.linspace(0, 100, 100)
x2 = np.random.rand(100)
x3 = np.cos(np.pi * np.linspace(0, 100, 1000))
r = tj.Trajectory()
self.assertAlmostEqual(r.efficiency_(x1), 1.0, places=2)
self.assertAlmostEqual(r.efficiency_(x2), 0.0, places=2)
self.assertAlmostEqual(r.straightness_(x3), 0.0, places=1)
def test_anomalous_diffusion(self):
"""
Test if the generator of anomalous trajectories is working properly
by measuring the fractal dimension.
"""
n_steps = 250
n_samples = 1
dt = 1.
xa, trajectory = tjg.anomalous_diffusion(n_steps,
n_samples,
dt,
alpha=1.0)
r = tj.Trajectory()
fractal_dimension, d_max = r.fractal_dimension_(trajectory)
self.assertAlmostEqual(fractal_dimension, 3.08, places=1)
def test_superdiffusion(self):
"""
Test if the generator of superdiffusion trajectories is working properly
by measuring the fractal dimension.
"""
n_steps = 250
n_samples = 1
dt = 0.1
velocity = 1.0
xa, trajectory = tjg.superdiffusion(velocity, n_steps, n_samples, 0.,
dt)
r = tj.Trajectory()
fractal_dimension, d_max = r.fractal_dimension_(trajectory)
self.assertAlmostEqual(np.around(fractal_dimension, decimals=0),
1.0,
places=1)
def test_confined_diffusion(self):
"""
Test if the generator of confined diffusion trajectories is working properly
by measuring the fractal dimension.
"""
n_steps = 250
n_samples = 1
dt = 0.1
D = 100.0
radius = 5.
xa, trajectory = tjg.confined_diffusion(radius, n_steps, n_samples,
1.0, 0., D, dt)
r = tj.Trajectory()
fractal_dimension, d_max = r.fractal_dimension_(trajectory)
self.assertAlmostEqual(np.around(fractal_dimension, decimals=0),
3.0,
places=1)
def open(self):
self.r = tj.Trajectory(self.path, skip_header=1, delimiter=',')
import trajpy.trajpy as tj
filename = '../data/samples/sample.csv'
r = tj.Trajectory(filename, skip_header=1, delimiter=',')
r.compute_features()
print(r.efficiency, r.asymmetry, r.gaussianity, r.straightness,
r.gyration_radius, r.kurtosis, r.anisotropy)
