def map(self, X):
""" Maps X (configuration or trajectory) to reference structure by permuting identical particles """
X = ensure_traj(X)
Y = X.copy()
C = distance_matrix_squared(np.tile(self.xref[:, self.ip_indices], (X.shape[0], 1)), X[:, self.ip_indices])
for i in range(C.shape[0]): # for each configuration
_, col_assignment = linear_sum_assignment(C[i])
assignment_components = [self.dim*col_assignment+i for i in range(self.dim)]
col_assignment = np.vstack(assignment_components).T.flatten()
Y[i, self.ip_indices] = X[i, self.ip_indices[col_assignment]]
return Y
def is_permuted(self, X):
""" Returns True for permuted configurations """
X = ensure_traj(X)
C = distance_matrix_squared(np.tile(self.xref[:, self.ip_indices], (X.shape[0], 1)), X[:, self.ip_indices])
isP = np.zeros(X.shape[0], dtype=bool)
for i in range(C.shape[0]): # for each configuration
_, col_assignment = linear_sum_assignment(C[i])
assignment_components = [self.dim*col_assignment+i for i in range(self.dim)]
col_assignment = np.vstack(assignment_components).T.flatten()
if not np.all(col_assignment == np.arange(col_assignment.size)):
isP[i] = True
return isP
def _distance_squared_matrix(self, crd1, crd2):
return distance_matrix_squared(crd1, crd2, dim=2)