def run_experiment(seed=None, graph_type=None, threshold=None, param_key=None):
np.random.seed(seed)
if BLIND:
temp_param_key = param_key.replace(
" ", "") # don't want spaces in filenames
savename = f"{temp_param_key}-cell-types-"
title = param_key
else:
savename = f"{graph_type}-t{threshold}-cell-types"
title = f"{graph_type}, threshold = {threshold}"
mg = load_metagraph(graph_type, version=VERSION)
# simple threshold
# TODO they will want symmetric threshold...
# TODO maybe make that a parameter
adj = mg.adj.copy()
adj[adj <= threshold] = 0
meta = mg.meta.copy()
meta = pd.DataFrame(mg.meta["neuron_name"])
mg = MetaGraph(adj, meta)
# run the graphtool code
temp_loc = f"maggot_models/data/interim/temp-{param_key}.graphml"
block_series = run_minimize_blockmodel(mg, temp_loc)
# manage the output
mg = load_metagraph(graph_type, version=VERSION)
mg.meta = pd.concat((mg.meta, block_series), axis=1)
mg.meta["Original index"] = range(len(mg.meta))
keep_inds = mg.meta[~mg.meta["block_label"].isna(
)]["Original index"].values
mg.reindex(keep_inds)
if graph_type != "G":
mg.verify(10000, graph_type=graph_type, version=VERSION)
# deal with class labels
lineage_labels = mg.meta["lineage"].values
lineage_labels = np.vectorize(lambda x: "~" + x)(lineage_labels)
class_labels = mg["Merge Class"]
skeleton_labels = mg.meta.index.values
classlin_labels, color_dict, hatch_dict = augment_classes(
skeleton_labels, class_labels, lineage_labels)
block_label = mg["block_label"].astype(int)
# barplot with unknown class labels merged in, proportions
_, _, order = barplot_text(
block_label,
classlin_labels,
norm_bar_width=True,
color_dict=color_dict,
hatch_dict=hatch_dict,
title=title,
figsize=(24, 18),
return_order=True,
)
stashfig(savename + "barplot-mergeclasslin-props")
category_order = np.unique(block_label)[order]
# barplot with regular class labels
barplot_text(
block_label,
class_labels,
norm_bar_width=True,
color_dict=color_dict,
hatch_dict=hatch_dict,
title=title,
figsize=(24, 18),
category_order=category_order,
)
stashfig(savename + "barplot-mergeclass-props")
# barplot with unknown class labels merged in, counts
barplot_text(
block_label,
classlin_labels,
norm_bar_width=False,
color_dict=color_dict,
hatch_dict=hatch_dict,
title=title,
figsize=(24, 18),
return_order=True,
category_order=category_order,
)
stashfig(savename + "barplot-mergeclasslin-counts")
# barplot of hemisphere membership
fig, ax = plt.subplots(1, 1, figsize=(10, 20))
stacked_barplot(
block_label,
mg["Hemisphere"],
norm_bar_width=True,
category_order=category_order,
ax=ax,
)
remove_spines(ax)
stashfig(savename + "barplot-hemisphere")
# plot block probability matrix
counts = False
weights = False
prob_df = get_blockmodel_df(mg.adj,
block_label,
return_counts=counts,
use_weights=weights)
prob_df = prob_df.reindex(order, axis=0)
prob_df = prob_df.reindex(order, axis=1)
ax = probplot(100 * prob_df,
fmt="2.0f",
figsize=(20, 20),
title=title,
font_scale=0.4)
stashfig(savename + "probplot")
block_series.name = param_key
return block_series
)
stashfig("heatmap")
heatmap(
mg.adj,
sort_nodes=False,
inner_hier_labels=mg["Hemisphere"],
transform="binarize",
cbar=False,
figsize=(30, 30),
hier_label_fontsize=10,
)
n_pairs = mg.adj.shape[0] // 2
mg.verify()
# %% [markdown]
# # Extract subgraphs
adj = mg.adj.copy()
threshold = 0
left_left_adj = adj[:n_pairs, :n_pairs]
left_right_adj = adj[:n_pairs, n_pairs : 2 * n_pairs]
right_right_adj = adj[n_pairs : 2 * n_pairs, n_pairs : 2 * n_pairs]
right_left_adj = adj[n_pairs : 2 * n_pairs, :n_pairs]
# %% [markdown]
# # Plot all edges, weights
