def __init__(self, net, pose, k=15, fix=None, dropout=0.1, steps=100):
self.k = k
self.fix = fix or []
self.fix = torch.tensor(self.fix, dtype=torch.long)
self.net = net
self.tertiary, self.angles, _, self.mask = pose_to_net(pose)
rotations, indices = nearest_neighbours(self.tertiary, k=self.k)
self.tertiary = self.tertiary.permute(2, 0, 1)
self.distances = self.tertiary[:, 1]
self.angles = self.angles.permute(1, 0)
orientations = orientation(
self.tertiary[:, 1].permute(1, 0)
).permute(2, 0, 1).view(self.tertiary.size(0), -1)
positions = torch.arange(0, self.tertiary.size(0)).float()[:, None]
self.orientations = torch.cat((self.distances, orientations, positions), dim=1)
self.structure = ts.ConstantStructure(0, 0, indices)
sin = torch.sin(self.angles)
cos = torch.cos(self.angles)
self.angle_features = torch.cat((sin, cos), dim=1)
self.sequence = pose.sequence()
self.best = 0.0
self.steps = steps
self.lookup = sorted(list(AA_ID_DICT.keys()), key=AA_ID_DICT.get)
def __init__(self, path, num_neighbours=20, n_jobs=1, cache=True):
ProteinNetKNN.__init__(self,
path,
num_neighbours=num_neighbours,
n_jobs=n_jobs,
cache=cache)
self.ors = torch.tensor(orientation(self.ters[1].numpy() /
100).transpose(2, 0, 1),
dtype=torch.float)
def __init__(self, path, num_neighbours=20, n_jobs=1, N=64, cache=True):
ProteinNetKNN.__init__(self,
path,
num_neighbours=num_neighbours,
n_jobs=n_jobs,
cache=cache)
self.backrub = Backrub(n_moves=0)
self.ors = torch.tensor(orientation(self.ters[1].numpy() /
100).transpose(2, 0, 1),
dtype=torch.float)
self.N = N
self.valid_indices = [
index for index in range(len(self.index) - 1)
if self.index[index + 1] - self.index[index] >= N
]
def __init__(self, path, num_neighbours=20, n_jobs=1, n_backrub=10, N=200,
phi=0.2 * np.pi, psi=0.2 * np.pi, tau=0.2 * np.pi, cache=True,
pass_mask=False):
super(FoldNet, self).__init__(
path,
num_neighbours=num_neighbours,
n_jobs=n_jobs, cache=cache
)
self.pass_mask = pass_mask
self.N = N
self.backrub = Backrub(n_moves=n_backrub, phi=phi, psi=psi, tau=tau)
self.ors = torch.tensor(
orientation(self.ters[1].numpy() / 100).transpose(2, 0, 1),
dtype=torch.float
)
def __init__(self,
path,
radius=8,
num_neighbours=15,
n_jobs=1,
cache=True):
ProteinNetKNN.__init__(self,
path,
num_neighbours=num_neighbours,
n_jobs=n_jobs,
cache=cache)
self.radius = radius
self.backrub = Backrub(n_moves=0) #10, phi=0.3, psi=0.3, tau=0.3)
self.ors = torch.tensor(orientation(self.ters[1].numpy() /
100).transpose(2, 0, 1),
dtype=torch.float)
def orientations(self, tertiary): ors = orientation(tertiary.permute(1, 0)).permute(2, 0, 1).contiguous() return ors.view(tertiary.size(0), 3, 3)
def orientations(self, tertiary):
ors = orientation(tertiary[:, 1].permute(1, 0)).permute(2, 0, 1)
return ors.view(tertiary.size(0), -1)
