# ##
# 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)
