plots.db import SCDB

from lisc.utils.db import SCDB
from lisc.utils.io import load_object

from lisc.plts.counts import plot_matrix, plot_clustermap, plot_dendrogram
# Reload the counts object from the last tutorial
counts = load_object('tutorial_counts', SCDB('lisc_db'))

# Reload the counts object from the last tutorial
counts = load_object('tutorial_counts', SCDB('lisc_db'))
# Compute a normalization of the co-occurrence data
counts.compute_score('normalize', dim='A')

# Compute the association index
counts.compute_score('association')

# Plot a matrix of the association index data
plot_matrix(counts.score, counts.terms['B'].labels, counts.terms['A'].labels)

# save the clustermap plot of the association index data
plot= plot_clustermap(counts.score, counts.terms['B'].labels, counts.terms['A'].labels)

save_object(plot, 'plot_clustermap.png', directory=SCDB('lisc_db'))