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()
def plot_3view(
data,
axs,
palette=None,
connectors=False,
connectors_only=False,
label_by=None,
alpha=1,
s=1,
row_title=None,
**kws,
):
volumes = [pymaid.get_volume(v) for v in volume_names]
for i, view in enumerate(views):
ax = axs[i]
if label_by is None:
pymaid.plot2d(
data,
color=palette,
ax=ax,
method="3d",
connectors=connectors,
connectors_only=connectors_only,
**kws,
)
else:
uni_labels = np.unique(data[label_by])
for ul in uni_labels:
temp_data = data[data[label_by] == ul]
color = palette[ul]
scatter_kws = dict(s=s, alpha=alpha, color=color)
pymaid.plot2d(
temp_data[["x", "y", "z"]],
ax=ax,
method="3d",
connectors=connectors,
connectors_only=connectors_only,
scatter_kws=scatter_kws,
**kws,
)
set_view_params(ax, **view_dict[view])
plot_volumes(volumes, ax)
if row_title is not None:
ax = axs[0]
ax.text2D(
x=0,
y=0.5,
s=row_title,
ha="right",
va="center",
color="grey",
rotation=90,
transform=ax.transAxes,
)
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(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,
def plot_neuron_morphology(ids, inputs, outputs, axs, row_label=True):
# plot neuron skeletons
row = 0
for i, view in enumerate(views):
ax = axs[row, i]
if show_neurons:
pymaid.plot2d(ids,
color=skeleton_color_dict,
ax=ax,
connectors=False,
method="3d")
plot_volumes(ax)
set_view_params(ax, **view_dict[view])
# plot inputs
row = 1
for i, view in enumerate(views):
ax = axs[row, i]
for j, ct in enumerate(connection_types):
ct_inputs = inputs[inputs["postsynaptic_type"] == ct]
connector_locs = ct_inputs[["x", "y", "z"]].values
pymaid.plot2d(
connector_locs,
ax=ax,
method="3d",
scatter_kws=dict(color=connection_colors[ct]),
)
plot_volumes(ax)
set_view_params(ax, **view_dict[view])
# plot outputs
row = 2
for i, view in enumerate(views):
ax = axs[row, i]
for j, ct in enumerate(connection_types):
ct_outputs = outputs[outputs["presynaptic_type"] == ct]
connector_locs = ct_outputs[["x", "y", "z"]].values
pymaid.plot2d(
connector_locs,
ax=ax,
method="3d",
scatter_kws=dict(color=connection_colors[ct],
label=ct,
s=2,
alpha=0.5),
)
# if i == 0:
# ax.legend(bbox_to_anchor=(0, 1), loc="upper left")
plot_volumes(ax)
set_view_params(ax, **view_dict[view])
if row_label:
axs[0, 0].text2D(
x=0,
y=0.5,
s="Skeletons",
ha="right",
va="center",
color="grey",
rotation=90,
transform=axs[0, 0].transAxes,
)
axs[1, 0].text2D(
x=0,
y=0.5,
s="Inputs",
ha="right",
va="center",
color="grey",
rotation=90,
transform=axs[1, 0].transAxes,
)
axs[2, 0].text2D(
x=0,
y=0.5,
s="Outputs",
ha="right",
va="center",
color="grey",
rotation=90,
transform=axs[2, 0].transAxes,
)
def plot_volumes(ax):
pymaid.plot2d(volumes, ax=ax, method="3d")
for c in ax.collections:
if isinstance(c, Poly3DCollection):
c.set_alpha(0.03)
def plot_neuron_morphology(ids, inputs, outputs, axs, row_label=True):
# plot neuron skeletons
row = 0
for i, view in enumerate(views):
ax = axs[row, i]
if show_neurons:
pymaid.plot2d(ids,
color=skeleton_color_dict,
ax=ax,
connectors=False,
method="3d")
plot_volumes(ax)
set_view_params(ax, **view_dict[view])
# plot inputs
row = 1
for i, view in enumerate(views):
ax = axs[row, i]
connector_locs = inputs[["x", "y", "z"]].values
pymaid.plot2d(connector_locs, color="orchid", ax=ax, method="3d")
plot_volumes(ax)
set_view_params(ax, **view_dict[view])
# plot outputs
row = 2
for i, view in enumerate(views):
ax = axs[row, i]
connector_locs = outputs[["x", "y", "z"]].values
pymaid.plot2d(connector_locs,
color="orchid",
ax=ax,
method="3d",
cn_mesh_colors=True)
plot_volumes(ax)
set_view_params(ax, **view_dict[view])
if row_label:
axs[0, 0].text2D(
x=0,
y=0.5,
s="Skeletons",
ha="center",
va="bottom",
color="grey",
rotation=90,
transform=axs[0, 0].transAxes,
)
axs[1, 0].text2D(
x=0,
y=0.5,
s="Inputs",
ha="center",
va="bottom",
color="grey",
rotation=90,
transform=axs[1, 0].transAxes,
)
axs[2, 0].text2D(
x=0,
y=0.5,
s="Outputs",
ha="center",
va="bottom",
color="grey",
rotation=90,
transform=axs[2, 0].transAxes,
)
x=0.1,
y=0.8,
s="Skeletons",
ha="center",
va="bottom",
color="grey",
rotation=90,
transform=fig.transFigure,
)
# plot inputs
row = 1
for i, view in enumerate(views):
ax = add_subplot(row, i)
connector_locs = inputs[["x", "y", "z"]].values
pymaid.plot2d(connector_locs, color="orchid", ax=ax, method="3d")
plot_volumes(ax)
set_view_params(ax, **view_dict[view])
axs[1, 0].text2D(
x=0.1,
y=0.49,
s="Inputs",
ha="center",
va="bottom",
color="grey",
rotation=90,
transform=fig.transFigure,
)
# plot outputs
ax=ax,
norm_bar_width=False,
)
stashfig("lvl2-barplot" + sub_basename)
# %% [markdown]
# ##
import pymaid
from src.pymaid import start_instance
start_instance()
for tp in meta["total_pred"].unique()[:10]:
ids = list(meta[meta["total_pred"] == tp].index.values)
ids = [int(i) for i in ids]
fig, ax = plt.subplots(1, 1, figsize=(10, 10))
skeleton_color_dict = dict(
zip(meta.index, np.vectorize(CLASS_COLOR_DICT.get)(meta["merge_class"]))
)
pymaid.plot2d(ids, color=skeleton_color_dict, ax=ax)
ax.axis("equal")
stashfig(f"test-plot2d-{tp}")
# %% [markdown]
# ##
# %%
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