def find_neighbours(self, position: np.array) -> np.array:
"""
Find neighbours of prospective position, including any overlapping sites
"""
distances = norm(self.position_buffer - position, axis=1)
neighbours = np.nonzero(
(distances == 1.) | (distances == np.sqrt(0.5) |
(distances == 0.))) #indexes of neighbours
return neighbours
def check_self_avoiding(self, index: int, new_pos: np.array) -> bool:
"""
Measure the distance between previous index and next index to maintain the backbone
"""
last_index = norm(new_pos -
self.position_buffer[(index - 1) %
len(self.position_buffer)]) in [
np.sqrt(0.5), 1.
]
next_index = norm(new_pos -
self.position_buffer[(index + 1) %
len(self.position_buffer)]) in [
np.sqrt(0.5), 1.
]
if index == 0:
return next_index
elif index == self.num_residues - 1:
return last_index
else:
return next_index and last_index
def calc_desireability(self, position: np.array,
num_neighbours: int) -> float:
"""
Calculate the desirebiilty of a prospective point according to agent density)
"""
position = position.reshape(-1, 1)
mean_pos = np.mean(self.position_buffer, axis=1).reshape(-1, 1)
Sigma = np.cov(self.position_buffer.T) #Covariance
offset = position - mean_pos.reshape(-1, 1)
numerator = np.exp(-offset.T.dot(inv(Sigma).dot(offset))).item()
coeff = (2 * pi * np.sqrt(norm(Sigma)))**-1
denom = (1 + (num_neighbours / 12)
)**-self.alpha #Max number of neighbours = 12 for FCC lattice
return coeff * numerator * denom
def compute_sim(self, feat1, feat2):
from np.linalg import norm
feat1 = feat1.ravel()
feat2 = feat2.ravel()
sim = np.dot(feat1, feat2) / (norm(feat1) * norm(feat2))
return sim