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
linearNorm = {} linearWeighted = {} for size in cluster_sizes: logNorm[size] = np.zeros(size) logWeighted[size] = np.zeros(size) #linearNorm[size] = np.zeros(size) #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)
linearNorm = {} linearWeighted = {} for size in cluster_sizes: logNorm[size] = np.zeros(size) logWeighted[size] = np.zeros(size) #linearNorm[size] = np.zeros(size) #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 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])
if Nchol: clusters[block][t]['chol'][layer] = {} t += nlog time += nlog #running for block in range(Nblock): start = block * nlog for time in times: t = start + time for size in cluster_sizes: upper_lipids, lower_lipids = trajIO.layering(com_lipids[t]) clusters[block][time]['lipids']['upper'][ size] = jenks_clusters.clusters(upper_lipids, size) clusters[block][time]['lipids']['lower'][ size] = jenks_clusters.clusters(lower_lipids, size) if Nchol: 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):
clusters[block][t]['chol'][layer] = {} t += nlog time += nlog #running for block in range(Nblock): start = block * nlog for time in times: 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