def setUp(self): self.sequence = ['GGGGGG', 'GGGGGG'] angles = torch.zeros(len(self.sequence), 7,len(self.sequence[0]), dtype=torch.double, device='cpu') angles[0,0,:] = -1.047 angles[0,1,:] = -0.698 angles[0,2:,:] = np.pi angles[0,3:,:] = 110.4*np.pi/180.0 a2c = Angles2Coords() self.coords, self.res_names, self.atom_names, self.num_atoms = a2c(angles, self.sequence) self.c2tc = Coords2TypedCoords()
def measure_trace(length=700, device='cuda'):
x0 = torch.randn(1, 3, length, dtype=torch.float, device=device)
x0.data[:, 2, :].fill_(-3.1318)
length = torch.zeros(1, dtype=torch.int, device=device).fill_(length)
a2c = Angles2Backbone()
proteins = a2c(x0, length)
proteins = proteins.data.cpu().resize_(1, 3 * length, 3).numpy()
a2cfa = Angles2Coords()
x0 = x0.cpu()
sequence = ''.join(['A' for i in range(length)])
x1 = torch.zeros(1, 7, length, dtype=torch.double)
x1.data[:, 0:2, :].copy_(x0.data[:, 0:2, :])
proteins_fa, res_names, atom_names, num_atoms = a2cfa(x1, [sequence])
proteins_fa = proteins_fa.data.cpu().resize_(1, num_atoms.data[0],
3).numpy()
error = []
index = []
k = 0
for i in range(num_atoms.data[0]):
if atom_names.data[0, i, 0] == 67 and atom_names.data[0, i,
1] == 0: #C
error.append(
np.linalg.norm(proteins[0, k, :] - proteins_fa[0, i, :]))
index.append(k)
k += 1
if atom_names.data[0, i, 0] == 67 and atom_names.data[
0, i, 1] == 65 and atom_names.data[0, i, 2] == 0: #CA
error.append(
np.linalg.norm(proteins[0, k, :] - proteins_fa[0, i, :]))
index.append(k)
k += 1
if atom_names.data[0, i, 0] == 78 and atom_names.data[0, i,
1] == 0: #N
error.append(
np.linalg.norm(proteins[0, k, :] - proteins_fa[0, i, :]))
index.append(k)
k += 1
return error, index
print(center)
centered_coords = translate(coords, -center, num_atoms)
a, b = getBBox(centered_coords, num_atoms)
center = (a + b) * 0.5
print(center)
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)
if __name__ == '__main__':
sequences = ['GGGGGG', 'GGAARRRRRRRRR']
angles = torch.zeros(2, 7, len(sequences[1]), dtype=torch.double)
angles[:, 0, :] = -1.047
angles[:, 1, :] = -0.698
angles[:, 2:, :] = 110.4 * np.pi / 180.0
a2c = Angles2Coords()
protein, res_names, atom_names, num_atoms = a2c(angles, sequences)
test_translation(protein, num_atoms)
test_rotation(protein, num_atoms)
import mpl_toolkits.mplot3d.axes3d as p3
import seaborn as sea
import torch.optim as optim
from TorchProteinLibrary.FullAtomModel import Angles2Coords
from TorchProteinLibrary.ReducedModel import Angles2Backbone
if __name__ == '__main__':
L = 700
x0 = torch.randn(1, 2, L, dtype=torch.float, device='cuda')
length = torch.zeros(1, dtype=torch.int, device='cuda').fill_(L)
a2c = Angles2Backbone()
proteins = a2c(x0, length)
proteins = proteins.data.cpu().resize_(1, 3 * L, 3).numpy()
a2cfa = Angles2Coords()
x0 = x0.cpu()
sequence = ''
for i in range(L):
sequence += 'A'
x1 = torch.zeros(1, 7, L, dtype=torch.double)
x1.data[:, 0:2, :].copy_(x0.data)
proteins_fa, res_names, atom_names, num_atoms = a2cfa(x1, [sequence])
proteins_fa = proteins_fa.data.cpu().resize_(1, num_atoms.data[0],
3).numpy()
error = []
k = 0
for i in range(num_atoms.data[0]):