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