def main():
file = "comTraj.npz"
L, com_lipids, com_chol = trajIO.decompress(file)
com_lipids, com_chol = trajIO.translateZ(com_lipids, com_chol)
com_lipids = displacement.block_displacement(L, com_lipids)
com_chol = displacement.block_displacement(L, com_chol)
t = 28
lipids = com_lipids[t]
chol = com_chol[t]
lipids, trash = trajIO.layering(lipids)
chol, trash = trajIO.layering(chol)
total = np.concatenate((lipids, chol), axis=0)
total1 = iter.combine(lipids, chol)
cluster = percentages.cluster(total, [0.25, 0.25, 0.25, 0.25])
cluster1 = percentages.cluster(total1, [0.25, 0.25, 0.25, 0.25])
#edm = euclideanDist.edm(L[t],cluster[0])
#edm1 = euclideanDist.edm(L[t],cluster1[0])
#print(np.array_equiv(edm,edm1))
cutoff = 1.15
labels1 = dc.dbscan_wrapper(cluster[0], L[t], cutoff)
labels2 = iter.cluster_labels('upper', L[t], cluster1[0])
return labels1, labels2
#linearWeighted[size] = np.zeros(size)
#block
for block in range(Nblock):
start = block * nlog
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
t = start + time
print(t) #progress tracker
ul, ll = trajIO.layering(com_lipids[t])
uc, lc = trajIO.layering(com_chol[t])
original = {}
original['upper'] = iter.combine(ul, uc)
original['lower'] = iter.combine(ll, lc)
random = {}
random['upper'] = (ul, uc)
random['lower'] = (ll, lc)
for layer in ['upper', 'lower']:
#clustering
clusters = percentages.cluster(
original[layer], c.percentages['all']['higher'][4])
for size in cluster_sizes:
for i in range(size):
nlipids, nchol = iter_cluster.counter(cluster[i])
normSizes[block][time][layer][size][
i], weightedNormSizes[block][time][layer][size][
i] = iter.mean_cluster_size(
clusters[i], L[t], flag)
if time == 1:
alpha, beta = iter.meanRandom(
original[layer], L[t], Nparticles, flag)
logNorm[size][i] += alpha
logWeighted[size][i] += beta