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
def spitnumpy(trajFileName, topology, Nconf):
outfile = "comTraj"
Nchol = trajIO.cholConc(topology)
N, L, com_lipids, com_chol = trajIO.processTrajCOM(trajFileName, Nchol,
c.NDIM, Nconf)
com_lipids, com_chol = trajIO.translateZ(com_lipids, com_chol)
trajIO.np.savez(outfile, L=L, com_lipids=com_lipids, com_chol=com_chol)
def test_randomCluster():
L, com_lipids, com_chol = trajIO.decompress("comTraj.npz")
com_lipids, com_chol = trajIO.translateZ(com_lipids, com_chol)
arr = com_lipids[0]
size = 43
cluster = iter_cluster.randomCluster(size, arr)
assert len(cluster) == 43
return
def spitnumpy(trajFileName1, trajFileName2, Nconf):
outfile = "tailTraj"
NDIM = 3
L = np.zeros((Nconf, NDIM), dtype=np.double)
com_lipids = np.zeros((Nconf, Nlipids, NDIM + 1), dtype=np.double)
com_chol = np.zeros((Nconf, Nchol, NDIM + 1), dtype=np.double)
trajFile1 = open(trajFileName1, 'r')
Nlipids = int(trajFile1.readline().strip())
print(Nlipids)
for t in range(Nconf):
time = trajFile1.readline()
assert t == time
line = trajFile1.readline().split()
for k in range(NDIM):
L[t, k] = float(line[k])
for i in range(Nlipids):
line = trajFile1.readline().split()
for k in range(NDIM + 1):
com_lipids[t][i][k] = line[k]
trajFile1.close()
trajFile2 = open(trajFileName2, 'r')
Nchol = int(trajFile2.readline().strip())
print(Nchol)
for t in range(Nconf):
time = trajFile.readline()
assert t == time
for i in range(Nchol):
line = trajFile.readline().split()
for k in range(NDIM + 1):
com_lipids[t][i][k] = line[k]
com_lipids, com_chol = trajIO.translateZ(com_lipids, com_chol)
trajIO.np.savez(outfile, L=L, com_lipids=com_lipids, com_chol=com_chol)
def test_buildCluster(): #& singletonMerge
L, com_lipids, com_chol = trajIO.decompress("comTraj.npz")
com_lipids, com_chol = trajIO.translateZ(com_lipids, com_chol)
Nlipids = len(com_lipids[0])
c = iter_cluster.Cluster(com_lipids[0], L[0], 0.3)
c.test()
"""
manualSingleton = set()
for cluster in c.clusters:
if len(cluster) == 1:
manualSingleton |= cluster
c.singletonMerge()
c.test()
assert c.clusters[-1] == manualSingleton
"""
return
if __name__ == '__main__':
name = 4
trajFileName = sys.argv[1]
Nconf = int(sys.argv[2])
nlog = int(sys.argv[3])
Nblock = Nconf // nlog
cutoff = 1.3 #anything above 20chol #maybe 1.3 for everything below?
percentage = c.percentages['lipids']['lower'][name]
if trajIO.rawOrCOM(trajFileName):
Nchol = trajIO.cholConc(topology)
N, L, com_lipids, com_chol = trajIO.processTrajCOM(
trajFileName, Nchol, c.NDIM, Nconf)
com_lipids, com_chol = trajIO.translateZ(com_lipids, com_chol)
Nlipids = com_lipids.shape[0]
else:
L, com_lipids, com_chol = trajIO.decompress(trajFileName)
com_lipids, com_chol = trajIO.translateZ(com_lipids, com_chol)
#parameters
del com_chol
cluster_sizes = [name]
name = str(cluster_sizes[0])
sys.stdout = open("norm" + name + ".txt", "w")
times = list(range(1, 46))
com_lipids = displacement.block_displacement(L, com_lipids)
def test_randomIterCluster():
L, com_lipids, com_chol = trajIO.decompress("comTraj.npz")
com_lipids, com_chol = trajIO.translateZ(com_lipids, com_chol)
size = 45
return
