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)
