# ## # parameters for the experiment metric = "bic" bic_ratio = 1 d = 8 # embedding dimension method = "iso" # parameters for plotting lowest_level = 7 width = 0.5 gap = 10 basename = f"-method={method}-d={d}-bic_ratio={bic_ratio}" title = f"Method={method}, d={d}, BIC ratio={bic_ratio}" exp = "137.0-BDP-omni-clust" # load data pair_meta = readcsv("meta" + basename, foldername=exp, index_col=0) pair_meta["lvl0_labels"] = pair_meta["lvl0_labels"].astype(str) pair_adj = readcsv("adj" + basename, foldername=exp, index_col=0) pair_mg = MetaGraph(pair_adj.values, pair_meta) pair_meta = pair_mg.meta lp_inds, rp_inds = get_paired_inds(pair_meta) left_adj = pair_adj[np.ix_(lp_inds, lp_inds)] right_adj = pair_adj[np.ix_(rp_inds, rp_inds)] calc_blockmodel_df
idx = mg.meta[mg.meta["hemisphere"].isin(["L", "R"])].index mg = mg.reindex(idx, use_ids=True) idx = mg.meta[mg.meta["Pair"].isin(mg.meta.index)].index mg = mg.reindex(idx, use_ids=True) mg = mg.make_lcc() mg.calculate_degrees(inplace=True) meta = mg.meta meta["pair_td"] = meta["Pair ID"].map( meta.groupby("Pair ID")["Total degree"].mean()) mg = mg.sort_values(["pair_td", "Pair ID"], ascending=False) meta["inds"] = range(len(meta)) adj = mg.adj.copy() lp_inds, rp_inds = get_paired_inds(meta) left_inds = meta[meta["left"]]["inds"] # %% from src.hierarchy import signal_flow sf = signal_flow(adj) fig, ax = plt.subplots(1, 1, figsize=(8, 8)) sns.scatterplot(x=sf[lp_inds], y=sf[rp_inds], ax=ax, s=15, linewidth=0, alpha=0.8)