def test_distarray(self):
from MDAnalysis.core.distances import distance_array
from MDAnalysis.core.distances import transform_StoR, transform_RtoS
R_mol1 = transform_StoR(self.S_mol1, self.box)
R_mol2 = transform_StoR(self.S_mol2, self.box)
# Try with box
dists = distance_array(R_mol1, R_mol2, box=self.box)
# Manually calculate distance_array
manual = np.zeros((len(R_mol1), len(R_mol2)))
for i, Ri in enumerate(R_mol1):
for j, Rj in enumerate(R_mol2):
Rij = Rj - Ri
Rij -= round(Rij[2] / self.box[2][2]) * self.box[2]
Rij -= round(Rij[1] / self.box[1][1]) * self.box[1]
Rij -= round(Rij[0] / self.box[0][0]) * self.box[0]
Rij = np.linalg.norm(Rij) # find norm of Rij vector
manual[i][j] = Rij
assert_almost_equal(dists, manual, self.prec,
err_msg="distance_array failed with box")
# Now check using boxV
dists = distance_array(R_mol1, R_mol2, box=self.boxV)
assert_almost_equal(dists, manual, self.prec,
err_msg="distance_array failed with boxV")
def test_selfdist(self):
from MDAnalysis.core.distances import self_distance_array
from MDAnalysis.core.distances import transform_RtoS, transform_StoR
R_coords = transform_StoR(self.S_mol1, self.box)
# Transform functions are tested elsewhere so taken as working here
dists = self_distance_array(R_coords, box=self.box)
# Manually calculate self_distance_array
manual = np.zeros(len(dists), dtype=np.float64)
distpos = 0
for i, Ri in enumerate(R_coords):
for Rj in R_coords[i + 1:]:
Rij = Rj - Ri
Rij -= round(Rij[2] / self.box[2][2]) * self.box[2]
Rij -= round(Rij[1] / self.box[1][1]) * self.box[1]
Rij -= round(Rij[0] / self.box[0][0]) * self.box[0]
Rij = np.linalg.norm(Rij) # find norm of Rij vector
manual[distpos] = Rij # and done, phew
distpos += 1
assert_almost_equal(dists, manual, self.prec,
err_msg="self_distance_array failed with input 1")
# Do it again for input 2 (has wider separation in points)
# Also use boxV here in self_dist calculation
R_coords = transform_StoR(self.S_mol2, self.box)
# Transform functions are tested elsewhere so taken as working here
dists = self_distance_array(R_coords, box=self.boxV)
# Manually calculate self_distance_array
manual = np.zeros(len(dists), dtype=np.float64)
distpos = 0
for i, Ri in enumerate(R_coords):
for Rj in R_coords[i + 1:]:
Rij = Rj - Ri
Rij -= round(Rij[2] / self.box[2][2]) * self.box[2]
Rij -= round(Rij[1] / self.box[1][1]) * self.box[1]
Rij -= round(Rij[0] / self.box[0][0]) * self.box[0]
Rij = np.linalg.norm(Rij) # find norm of Rij vector
manual[distpos] = Rij # and done, phew
distpos += 1
assert_almost_equal(dists, manual, self.prec,
err_msg="self_distance_array failed with input 2")
def test_transforms(self):
from MDAnalysis.core.distances import transform_StoR, transform_RtoS
# To check the cython coordinate transform, the same operation is done in numpy
# Is a matrix multiplication of Coords x Box = NewCoords, so can use np.dot
# Test transformation
R_mol1 = transform_StoR(self.S_mol1, self.box)
R_np1 = np.dot(self.S_mol1, self.box)
# Test transformation when given box in different form
R_mol2 = transform_StoR(self.S_mol2, self.boxV)
R_np2 = np.dot(self.S_mol2, self.box)
assert_almost_equal(R_mol1, R_np1, self.prec, err_msg="StoR transform failed with box")
assert_almost_equal(R_mol2, R_np2, self.prec, err_msg="StoR transform failed with boxV")
# Round trip test
S_test1 = transform_RtoS(R_mol1, self.boxV) # boxV here althought initial transform with box
S_test2 = transform_RtoS(R_mol2, self.box) # and vice versa, should still work
assert_almost_equal(S_test1, self.S_mol1, self.prec, err_msg="Round trip failed in transform")
assert_almost_equal(S_test2, self.S_mol2, self.prec, err_msg="Round trip failed in transform")