hspace=0, left=margin, right=margin + 3 / n_col, top=1 - margin, bottom=margin, ) morpho_axs = np.empty((1, 3), dtype="O") i = 0 for j in range(3): ax = fig.add_subplot(morpho_gs[i, j], projection="3d") morpho_axs[i, j] = ax ax.axis("off") return fig, morpho_axs skeleton_color_dict = dict( zip(meta.index, np.vectorize(CLASS_COLOR_DICT.get)(meta["merge_class"]))) start_instance() for i, component_neurons in enumerate(all_component_neurons): print(i) fig, axs = make_figure_axes() plot_3view( list(component_neurons), axs[0, :], palette=skeleton_color_dict, row_title="", ) stashfig(f"component_{i+1}_morphology")
name = filt(annot_name) else: name = annot_name indicator = pd.Series(index=ids, data=np.ones(len(ids), dtype=bool), name=name, dtype=bool) series_ids.append(indicator) print() return pd.concat(series_ids, axis=1, ignore_index=False) # %% [markdown] # ## load main groups as boolean columns start_instance() # creates a pymaid instance def filt(name): name = name.replace("mw ", "") name = name.replace(" ", "_") return name meta = df_from_meta_annotation("mw neuron groups", filt=filt) output_meta = df_from_meta_annotation("mw brain outputs", filt=filt) is_output = output_meta.any(axis=1) meta.loc[is_output.index, "output"] = is_output input_meta = df_from_meta_annotation("mw brain inputs", filt=filt)