def linear_test():
file = "comTraj.npz"
L,com_lipids,com_chol = trajIO.decompress(file)
control = displacement.block_displacement(L,com_lipids)
control1 = control[:,:,3]
test = displacement.linear_displacement(L,control,0,100)
test1 = test[:,:,3]
print(test1.shape)
for t in range(46):
boo = (test1[t] == control1[t])
print(boo.all())
print("hi")
for t in [46,92]:
boo = (test1[t] != control1[t])
print(boo.all())
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)
for time in linear_t:
#print(start,time)
t = start + time
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:
t = start + time
for size in cluster_sizes:
upper_chol, lower_chol = trajIO.layering(com_chol[t])
clusters[block][time]['chol']['upper'][
size] = jenks_clusters.clusters(upper_chol, size)
clusters[block][time]['chol']['lower'][
size] = jenks_clusters.clusters(lower_chol, size)
for block in range(Nblock):
start = block * nlog
linear_t = displacement.linear_gen(start, Nconf)
com_chol = displacement.linear_displacement(L, com_chol, start, Nconf)
for time in linear_t:
t = start + time
for size in cluster_sizes:
upper_chol, lower_chol = trajIO.layering(com_chol[t])
clusters[block][time]['chol']['upper'][
size] = jenks_clusters.clusters(upper_chol, size)
clusters[block][time]['chol']['lower'][
size] = jenks_clusters.clusters(lower_chol, size)
output = "clusters_chol.dict"
f = open(output, "wb")