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