def test_rotation(coords, num_atoms):
batch_size = num_atoms.size(0)
R = getRandomRotation(batch_size)
rotate = CoordsRotate()
rotated = rotate(coords, R, num_atoms)
print(rotated)
def setUp(self): print(self.msg, self.device, self.dtype) self.translate = CoordsTranslate() self.rotate = CoordsRotate() self.getCenter = Coords2Center() self.coords = torch.randn(self.batch_size, self.max_num_atoms*3, dtype=self.dtype, device=self.device) self.num_atoms = torch.zeros(self.batch_size, dtype=torch.int, device=self.device).random_(int(self.max_num_atoms/2), self.max_num_atoms)
def test_random_rotation(num_rotations): batch_size = num_rotations R = getRandomRotation(batch_size) num_atoms = torch.ones(R.size(0), dtype=torch.int) coords = torch.zeros(R.size(0),3, dtype=torch.double) coords[:,0]=1.0 coords[:,1]=0.0 coords[:,2]=0.0 rotate = CoordsRotate() rotated = rotate(coords, R, num_atoms) return rotated
def test_rotation(angle_inc = 0.1): u = [] for alpha in np.arange(0.0, 1.0, angle_inc): for beta in np.arange(0.0, 1.0, angle_inc): for gamma in np.arange(0.0, 1.0, angle_inc): u.append([alpha, beta, gamma]) u = np.array(u) u = torch.from_numpy(u) u = u.to(dtype=torch.double) R = getRotation(u) rotate = CoordsRotate() num_atoms = torch.ones(u.size(0), dtype=torch.int) coords = torch.zeros(u.size(0),3, dtype=torch.double) coords[:,0]=1.0 coords[:,1]=0.0 coords[:,2]=0.0 rotated = rotate(coords, R, num_atoms) return rotated
def setUp(self):
print(self.msg, self.device, self.dtype)
self.a2c = Angles2Coords()
self.translate = CoordsTranslate()
self.rotate = CoordsRotate()
self.rmsd = Coords2RMSD()
if __name__=='__main__': box_size = 80 resolution = 1.25 sequence = ['GGAGRRRGGWG'] angles = torch.zeros(len(sequence), 7, len(sequence[0]), dtype=torch.double) angles[0,0,:] = -1.047 angles[0,1,:] = -0.698 angles[0,2:,:] = 110.4*np.pi/180.0 a2c = Angles2Coords() c2cc = Coords2CenteredCoords(rotate=False, translate=False, box_size=box_size, resolution=resolution) c2tc = Coords2TypedCoords() tc2v = TypedCoords2Volume(box_size=box_size, resolution=resolution) rotate = CoordsRotate() translate = CoordsTranslate() R = getRandomRotation( len(sequence) ) volume_rotate = VolumeRotation(mode='bilinear') #Generating protein and initial volume protein, res_names, atom_names, num_atoms = a2c(angles, sequence) protein = c2cc(protein, num_atoms) coords, num_atoms_of_type, offsets = c2tc(protein, res_names, atom_names, num_atoms) volume = tc2v(coords.cuda(), num_atoms_of_type.cuda(), offsets.cuda()) #Rotating protein center = torch.zeros(len(sequence), 3, dtype=torch.double, device='cpu').fill_((box_size - 0.5)*resolution/2.0) centered_coords = translate(coords, -center, num_atoms) rotated_centered_coords = rotate(centered_coords, R, num_atoms)