Manipulate Morphology#

import navis

n = navis.example_neurons(1, kind="skeleton")
print(n.soma)

navis.reroot_skeleton(n, n.soma, inplace=True)

sk = navis.example_neurons(n=1, kind="skeleton")
print(sk.n_nodes)

sk_downsampled = navis.downsample_neuron(sk, downsampling_factor=10, inplace=False)
print(sk_downsampled.n_nodes)

me = navis.example_neurons(n=1, kind="mesh")
print(me.n_faces)

me_downsampled = navis.downsample_neuron(me, downsampling_factor=10, inplace=False)
print(me_downsampled.n_faces)

sk = navis.example_neurons(n=1, kind="skeleton")
print(sk.sampling_resolution * sk.units)

sk_resampled = navis.resample_skeleton(sk, resample_to="1 micron", inplace=False)
print(sk_resampled.sampling_resolution * sk_resampled.units)

import matplotlib.pyplot as plt

nodes_original = sk.nodes[["x", "y", "z"]].values
nodes_downsampled = sk_downsampled.nodes[["x", "y", "z"]].values
nodes_resampled = sk_resampled.nodes[["x", "y", "z"]].values

fig, axes = plt.subplots(1, 3, figsize=(18, 5))

_ = navis.plot2d(
    nodes_original,
    method="2d",
    view=("x", "-z"),
    scatter_kws=dict(c="blue"),
    ax=axes[0],
)
_ = navis.plot2d(
    nodes_resampled,
    method="2d",
    view=("x", "-z"),
    scatter_kws=dict(c="red"),
    ax=axes[1],
)
_ = navis.plot2d(
    nodes_downsampled,
    method="2d",
    view=("x", "-z"),
    scatter_kws=dict(c="green"),
    ax=axes[2],
)

for ax, title in zip(axes, ["Original", "Resampled to 1um", "Downsampled 10x"]):
    ax.set_title(title, color="k")
    ax.invert_yaxis()
    ax.set_axis_off()

plt.tight_layout()

# smooth_skeleton uses a rolling window along the linear segments
sk = navis.example_neurons(n=1, kind="skeleton")
sk_smoothed = navis.smooth_skeleton(sk, window=5, inplace=False)

# smooth_mesh uses a iterative rounds of Laplacian smoothing
me = navis.example_neurons(n=1, kind="mesh")
me_smoothed = navis.smooth_mesh(me, iterations=5, inplace=False)

# Load the neuron
n = navis.example_neurons(1, kind="skeleton")

# Pick a node ID
cut_node_id = n.nodes.node_id.values[333]
distal, proximal = navis.cut_skeleton(n, cut_node_id)

# Note that we are using method='2d' here because that makes annotating the plot easier
fig, ax = distal.plot2d(color="cyan", method="2d", view=("x", "-z"))
fig, ax = proximal.plot2d(color="green", ax=ax, method="2d", view=("x", "-z"))

# Annotate cut point
cut_coords = distal.nodes.set_index("node_id").loc[distal.root, ["x", "z"]].values[0]
ax.annotate(
    "cut point",
    xy=(cut_coords[0], -cut_coords[1]),
    color="lightgrey",
    xytext=(cut_coords[0], -cut_coords[1] - 2000),
    va="center",
    ha="center",
    arrowprops=dict(shrink=0.1, width=2, color="lightgrey"),
)

plt.tight_layout()

node_id, dist = n.snap([14000, 16200, 12000])
print(f"Closest node: {node_id} at distance {dist * n.units:.2f} {n.units.units}")

n_pruned = n.prune_distal_to(cut_node_id, inplace=False)

cut_coords = n.nodes.set_index("node_id").loc[cut_node_id, ["x", "z"]].values

# Plot original neuron in red and with dotted line
fig, ax = n.plot2d(color="red", method="2d", linestyle=(0, (5, 10)), view=("x", "-z"))

# Plot remaining neurites in red
fig, ax = n_pruned.plot2d(color="green", method="2d", ax=ax, view=("x", "-z"), lw=1.2)

# Annotate cut point
ax.annotate(
    "cut point",
    xy=(cut_coords[0], -cut_coords[1]),
    color="lightgrey",
    xytext=(cut_coords[0], -cut_coords[1] - 2000),
    va="center",
    ha="center",
    arrowprops=dict(shrink=0.1, width=2, color="lightgrey"),
)

plt.tight_layout()

n = navis.example_neurons(1, kind="skeleton")

branch_points = n.nodes[n.nodes.type == "branch"].node_id.values

cut = navis.cut_skeleton(n, branch_points)
cut.head()

# Plot neuron fragments
fig, ax = navis.plot2d(cut, linewidth=1.5, view=("x", "-z"))

plt.tight_layout()

# Load a fresh skeleton
n = navis.example_neurons(1, kind="skeleton")

# Reroot to soma
n = n.reroot(n.soma)

# This will prune off terminal branches (the lowest two Strahler indices)
n_pruned = n.prune_by_strahler(to_prune=[1, 2], inplace=False)

# Plot original neurons in red
fig, ax = n.plot2d(color="red", view=('x', '-z'))

# Plot remaining neurites in green
fig, ax = n_pruned.plot2d(color="green", ax=ax, linewidth=1, view=("x", "-z"))

plt.tight_layout()

m = navis.example_neurons(1, kind="mesh")

# This will prune to the just terminal branches
m_pruned = navis.prune_by_strahler(m, to_prune=range(3, 100), inplace=False)

# Plot original neuron in cyan
fig, ax = m.plot2d(color="cyan", figsize=(10, 10), view=("x", "-z"))

# Plot remaining neurites red
fig, ax = m_pruned.plot2d(color="red", ax=ax, view=("x", "-z"))

plt.tight_layout()

# This will prune all branches smaller than 10 microns
m_pruned = navis.prune_twigs(m, size="10 microns", inplace=False)

# Plot original neuron in red
fig, ax = m.plot2d(color="red", figsize=(10, 10), view=("x", "-z"))

# Plot remaining neurites in cyan
fig, ax = m_pruned.plot2d(
    color="cyan", ax=ax, linewidth=0.75, alpha=0.5, view=("x", "-z")
)

plt.tight_layout()

lh = navis.example_volume("LH")

# Prune by volume
m_lh = navis.in_volume(m, lh, inplace=False)
m_outside_lh = navis.in_volume(m, lh, mode="OUT", inplace=False)

fig, ax = navis.plot2d(
    [m_lh, m_outside_lh, lh], color=["red", "green"], figsize=(10, 10), view=("x", "-z")
)

plt.tight_layout()

# Note that [`navis.in_volume`][] also works with arbitrary spatial data (i.e. `(N, 3)` arrays of x/y/z locations):

cn = sk.connectors

# Add a column that tells us which connectors are in the LH volume
cn["in_lh"] = navis.in_volume(cn[["x", "y", "z"]].values, lh)
cn.head()

cn.groupby(["type", "in_lh"]).size()