print('Hierarchy version took %f seconds' % hierarchy_time)
def size():
"""
lst = []
for i in range(100):
for j in range(i):
lst.append(i)
"""
lst = list(range(100000))
return dc.means(dc.cluster_sizes(lst))
if __name__ == '__main__':
#benchmark()
L, com_lipids, com_chol = trajIO.decompress("comTraj.npz")
import pickle
pickle_off = open("clusters.dict","rb")
clusters = pickle.load(pickle_off)
X = clusters[0][28]['lipid'][0][0]
assert dc.mean_cluster_size(X,L[0],1.15,dc.dbscan_wrapper) == dc.mean_cluster_size(X,L[0],1.15,dc.hierarchy_wrapper)
#print(dc.mean_cluster_size(X,L[0],1.15,dc.dbscan_wrapper))
#print(dc.meanRandom(com_lipids[28],L[0],1.15,319,))
print(dc.normSize(X,L[0],1.15,com_lipids[0]))
#print(size())
#print(t) #progress tracker
upper, lower = trajIO.layering(com_lipids[t])
original = {}
original['upper'] = upper
original['lower'] = lower
for layer in ['upper', 'lower']:
#clustering
clusters = percentages.cluster(original[layer], percentage)
for size in cluster_sizes:
for i in range(size):
Nparticles = len(clusters[i])
normSizes[block][time][layer][size][
i], weightedNormSizes[block][time][layer][size][
i] = dc.mean_cluster_size(
clusters[i], L[t], cutoff)
if time == 1:
alpha, beta = dc.meanRandom(
original[layer], L[t], cutoff, Nparticles)
logNorm[size][i] += alpha
logWeighted[size][i] += beta
#linear
#linearNorm = linearWeighted =
for block in range(Nblock):
start = block * nlog
linear_t = displacement.linear_gen(start, Nconf)
com_lipids = displacement.linear_displacement(L, com_lipids, start,
for time in times:
t = start + time
#print(t) #progress tracker
upper,lower = trajIO.layering(com_lipids[t])
original = {}
original['upper'] = upper
original['lower'] = lower
for layer in ['upper','lower']:
#clustering
clusters = percentages.cluster(original[layer],percentage)
for size in cluster_sizes:
for i in range(size):
Nparticles = len(clusters[i])
normSizes[block][time][layer][size][i],weightedNormSizes[block][time][layer][size][i] = dc.mean_cluster_size(clusters[i],L[t],cutoff)
if time == 1:
alpha,beta = dc.meanRandom(original[layer],L[t],cutoff,Nparticles)
logNorm[size][i] += alpha
logWeighted[size][i] += beta
#linear
#linearNorm = linearWeighted =
for block in range(Nblock):
start = block*nlog
linear_t = displacement.linear_gen(start,Nconf)
com_lipids = displacement.linear_displacement(L,com_lipids,start,Nconf)