[1]:
from pathlib import Path
from brainlit.utils.Neuron_trace import NeuronTrace
from brainlit.algorithms.trace_analysis.fit_spline import GeometricGraph
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.cm import ScalarMappable
from mpl_toolkits.mplot3d import Axes3D
import matplotlib
from scipy.interpolate import splev
/opt/buildhome/python3.7/lib/python3.7/site-packages/nilearn/datasets/__init__.py:96: FutureWarning: Fetchers from the nilearn.datasets module will be updated in version 0.9 to return python strings instead of bytes and Pandas dataframes instead of Numpy arrays.
  "Numpy arrays.", FutureWarning)

Fitting Splines to Neuron Trace SWC Tutorial

1) Define variables

  • swc the geometric graph

  • df,_,_,_ read the x, y, and z columns in swc file

  • neuron define a new class inherited from GeometricGraph class

  • soma define the data on the first run as the location of soma

[2]:
brainlit_path=Path.cwd().parent.parent.parent
swc=Path.joinpath(brainlit_path,'data','data_octree','consensus-swcs','2018-08-01_G-002_consensus.swc')
nt = NeuronTrace(path=str(swc))
df = nt.get_df()
neuron = GeometricGraph(df=df)
soma = np.array([df.x[0], df.y[0], df.z[0]])

2) Plot the whole spline tree

  • spline_tree use the fit_spline_tree_invariant to locate neuron branches

[3]:
fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")
spline_tree = neuron.fit_spline_tree_invariant()
for node in spline_tree.nodes:
    path = spline_tree.nodes[node]["path"]
    locs = np.zeros((len(path),3))
    for p,point in enumerate(path):
        locs[p,:] = neuron.nodes[point]["loc"]
    ax.scatter(locs[:,0], locs[:,1], locs[:,2], marker=".",edgecolor='yellowgreen',linewidths=1,c='mediumblue', s=8)
    ax.plot(locs[:,0], locs[:,1], locs[:,2], linestyle='-',color='midnightblue',linewidth=0.8)
ax.scatter(soma[0],soma[1],soma[2], c='darkorange', s=5)
ax.w_xaxis.set_pane_color((1.0, 1.0, 1.0, 1.0))
ax.w_yaxis.set_pane_color((1.0, 1.0, 1.0, 1.0))
ax.w_zaxis.set_pane_color((1.0, 1.0, 1.0, 1.0))
ax.grid(True)


plt.show()
../../_images/notebooks_algorithms_view_swc_spline_4_0.png

Figure 1. The green dots indicate the locations of nodes on a tree, representing a neuron, and the single orange dot locates the position of the soma. Each nodes are connected with darkblue lines to illustrate the path of the neuron.

3) Plot each branch in separate plots

[4]:
for node in spline_tree.nodes:
    path = spline_tree.nodes[node]["path"]
    locs = np.zeros((len(path),3))
    for p,point in enumerate(path):
        locs[p,:] = neuron.nodes[point]["loc"]

    spline = spline_tree.nodes[node]["spline"]
    u = spline[1]
    u = np.arange(u[0], u[-1]+0.9, 1)
    tck = spline[0]
    pts = splev(u, tck)


    if node < 3:
        fig = plt.figure()
        ax = fig.add_subplot(111,projection="3d")
        ax.plot(pts[0], pts[1], pts[2], 'red')
        ax.w_xaxis.set_pane_color((0.23, 0.25, 0.209, 0.5))
        ax.w_yaxis.set_pane_color((0.23, 0.25, 0.209, 0.1))
        ax.w_zaxis.set_pane_color((0.23, 0.25, 0.209, 0.3))
        ax.grid(False)
        ax.set_xticks([])
        ax.set_yticks([])
        ax.set_zticks([])
        plt.axis('on')
        plt.show()
../../_images/notebooks_algorithms_view_swc_spline_7_0.png
../../_images/notebooks_algorithms_view_swc_spline_7_1.png
../../_images/notebooks_algorithms_view_swc_spline_7_2.png

Figure 2. Examples of fitted splines to three different paths in a tree-like neuron.