def clustergram(
adj, latent, prob_df, block_sum_df, true_labels, pred_labels, figsize=(20, 20)
):
fig, ax = plt.subplots(2, 2, figsize=figsize)
ax = ax.ravel()
sns.set_context("talk", font_scale=2)
color_dict = get_colors(true_labels, pred_labels)
sankey(
ax[0], true_labels, pred_labels, aspect=20, fontsize=16, colorDict=color_dict
)
ax[0].axis("off")
ax[0].set_title("Known class sorting", fontsize=30, pad=45)
ax[1] = heatmap(
adj,
transform="simple-all",
inner_hier_labels=pred_labels,
cbar=False,
sort_nodes=True,
ax=ax[1],
cmap="PRGn_r",
hier_label_fontsize=16,
)
ax[1].set_title("Sorted heatmap", fontsize=30, pad=70)
probplot(100 * prob_df, ax=ax[2], title="Connection percentage")
probplot(block_sum_df, ax=ax[3], title="Average synapses")
# skeleton_labels,
# class_labels,
# class_colors,
# multiout=True,
# )
title = (
f"Divisive hierarchical clustering," +
f" {cluster_type}, {embed_type} ({latent_dim} + {latent_dim}), {ptr_type},"
+ f" {brain_type}, {graph_type}")
fig, ax = plt.subplots(1, 1, figsize=(20, 30))
sankey(
ax,
class_labels[~is_sensory],
true_pred_labels,
aspect=20,
fontsize=16,
colorDict=all_color_dict,
)
ax.axis("off")
ax.set_title(title, fontsize=30)
stashfig("sankey" + name_base)
fig, ax = plt.subplots(1, 1, figsize=(20, 30))
sankey(
ax,
true_pred_labels,
class_labels[~is_sensory],
aspect=20,
fontsize=16,
colorDict=all_color_dict,
n_classes = len(np.unique(class_labels))
class_color_dict = get_color_dict(class_labels, pal=cc.glasbey_cool)
pred_color_dict = get_color_dict(pred_labels, pal=cc.glasbey_warm)
all_color_dict = {**class_color_dict, **pred_color_dict}
title = (
f"Divisive hierarchical clustering," +
f" {cluster_type}, {embed_type} ({latent_dim} + {latent_dim}), {ptr_type},"
+ f" {brain_type}, {graph_type}")
name_base = f"-{cluster_type}-{embed_type}-{ptr_type}-{brain_type}-{graph_type}"
fig, ax = plt.subplots(1, 1, figsize=(20, 30))
sankey(ax,
class_labels,
pred_labels,
aspect=20,
fontsize=16,
colorDict=all_color_dict)
ax.axis("off")
ax.set_title(title, fontsize=30)
fig, ax = plt.subplots(1, 1, figsize=(20, 30))
sankey(ax,
pred_labels,
class_labels,
aspect=20,
fontsize=16,
colorDict=all_color_dict)
ax.axis("off")
ax.set_title(title, fontsize=30)
pred_labels = skmeans.fit_predict(latent)
pred_labels = relabel(pred_labels)
fig, ax = plt.subplots(2, 2, figsize=(30, 30))
ax = ax.ravel()
heatmap(
binarize(adj),
inner_hier_labels=pred_labels,
# outer_hier_labels=side_labels,
hier_label_fontsize=18,
ax=ax[0],
cbar=False,
sort_nodes=True,
)
# uni_labels = np.unique(pred_labels)
sankey(ax[1], class_labels, pred_labels)
ax[1].axis("off")
rand_perm = np.random.permutation(len(pred_labels))
heatmap(
binarize(adj),
inner_hier_labels=pred_labels[rand_perm],
# outer_hier_labels=side_labels,
hier_label_fontsize=18,
ax=ax[2],
cbar=False,
sort_nodes=True,
)
sankey(ax[3], class_labels, pred_labels[rand_perm])
ax[3].axis("off")