def plot_fragments(nl, splits, neuron_class=None, scale=8):
n_col = len(nl)
fig = plt.figure(figsize=(scale * n_col,
scale)) # constrained_layout=True)
for i, n in enumerate(nl):
ax = fig.add_subplot(1, n_col, i + 1, projection="3d")
skid = int(n.skeleton_id)
if skid in splits.index:
split_nodes = splits[skid]
split_locs = pymaid.get_node_location(split_nodes)
split_locs = split_locs[["x", "y", "z"]].values
pymaid.plot2d(split_locs,
ax=ax,
scatter_kws=dict(color="orchid", s=30),
method="3d")
# order of output is axon, dendrite
fragments = pymaid.cut_neuron(n, split_nodes)
else:
fragments = [n]
n_frag = len(fragments)
for i, f in enumerate(fragments):
if n_frag == 1:
color = colors[2] # unsplitable
elif i == n_frag - 1:
color = colors[1] # dendrite
else:
color = colors[0] # axon
f.plot2d(ax=ax, color=color, method="3d")
title = f"{neuron_class}, {n.neuron_name}, {n.skeleton_id}"
ax.set_title(title, color="grey")
set_axes_equal(ax)
plt.tight_layout()
# ax = fig.add_subplot(1, 3, 1, projection="3d")
ax = fig.add_subplot(gs[0, 0], projection="3d")
pymaid.plot2d(
ids,
color=skeleton_color_dict,
ax=ax,
connectors=False,
method="3d",
autoscale=True,
)
ax.azim = -90 # 0 for side view
ax.elev = 0
ax.dist = 6
set_axes_equal(ax)
# ax = fig.add_subplot(1, 3, 2, projection="3d")
ax = fig.add_subplot(gs[0, 1], projection="3d")
pymaid.plot2d(
ids,
color=skeleton_color_dict,
ax=ax,
connectors=False,
method="3d",
autoscale=True,
)
ax.azim = 0 # 0 for side view
ax.elev = 0
ax.dist = 6
set_axes_equal(ax)
def set_view_params(ax, azim=-90, elev=0, dist=5):
ax.azim = azim
ax.elev = elev
ax.dist = dist
set_axes_equal(ax)
def plot_neurons(meta, key=None, label=None, barplot=False):
if label is not None:
ids = list(meta[meta[key] == label].index.values)
else:
ids = list(meta.index.values)
ids = [int(i) for i in ids]
new_ids = []
for i in ids:
try:
pymaid.get_neuron(
i, raise_missing=True, with_connectors=False, with_tags=False
)
new_ids.append(i)
except:
print(f"Missing neuron {i}, not plotting it.")
ids = new_ids
meta = meta.loc[ids]
fig = plt.figure(figsize=(30, 10))
gs = plt.GridSpec(2, 3, figure=fig, wspace=0, hspace=0, height_ratios=[0.8, 0.2])
skeleton_color_dict = dict(
zip(meta.index, np.vectorize(CLASS_COLOR_DICT.get)(meta["merge_class"]))
)
ax = fig.add_subplot(gs[0, 0], projection="3d")
pymaid.plot2d(
ids,
color=skeleton_color_dict,
ax=ax,
connectors=False,
method="3d",
autoscale=True,
)
ax.azim = -90
ax.elev = 0
ax.dist = 5
set_axes_equal(ax)
ax = fig.add_subplot(gs[0, 1], projection="3d")
pymaid.plot2d(
ids,
color=skeleton_color_dict,
ax=ax,
connectors=False,
method="3d",
autoscale=True,
)
ax.azim = 0
ax.elev = 0
ax.dist = 5
set_axes_equal(ax)
ax = fig.add_subplot(gs[0, 2], projection="3d")
pymaid.plot2d(
ids,
color=skeleton_color_dict,
ax=ax,
connectors=False,
method="3d",
autoscale=True,
)
ax.azim = -90
ax.elev = 90
ax.dist = 5
set_axes_equal(ax)
if barplot:
ax = fig.add_subplot(gs[1, :])
temp_meta = meta[meta[key] == label]
cat = temp_meta[key + "_side"].values
subcat = temp_meta["merge_class"].values
stacked_barplot(
cat,
subcat,
ax=ax,
color_dict=CLASS_COLOR_DICT,
category_order=np.unique(cat),
)
ax.get_legend().remove()
# fig.suptitle(label)
return fig, ax