In [118]:
# Imports
import sys
from neuron_readExportedGeometry import *
import matplotlib.pyplot as plt
%matplotlib inline
In [119]:
# Set Up the Geo Object
geo = demoReadsilent('/home/user/878_043_GM_scaled.hoc')
#Directory of the neuron hoc file
In [120]:
# Find and index the tips
tips, ends = geo.getTips()
In [121]:
# Calculate path distance to each tip
pDF = PathDistanceFinder(geo, geo.soma)
pdists = [pDF.distanceTo(seg) for seg in tips]
In [122]:
# Calculate Euclidean distance to each tip
def euc(pt0, pt1): # Function lifted from Alex's code
return np.sqrt(sum([(pt1[i]-pt0[i])**2 for i in range(3)]))
somacoord = geo.soma.coordAt(0)
coords = [tips[i].coordAt(ends[i]) for i in range(len(tips))]
edists = [euc(somacoord, coords[i]) for i in range(len(tips))]
In [123]:
# Calculate tortuosity for each tip
torts = [pdists[i]/edists[i] for i in range(len(tips))]
In [208]:
# Build skeleton
plt.figure(figsize=(16,18))
bpts = []
for b in geo.branches:
bpts.append([[n.x, n.y] for n in b.nodes])
for b in bpts:
for c in range(len(b)-1):
plt.plot([b[c][0], b[c+1][0]],
[b[c][1], b[c+1][1]], color='k', alpha=0.9)
# Plot points colored by path distance
maxDist = max(pdists)
tfloats = [float(i)/maxDist for i in pdists]
tcolors = plt.cm.jet(tfloats)
cmap = plt.cm.jet
for t in range(len(coords)):
plt.plot(coords[t][0], coords[t][1], 'o', color=tcolors[t],
alpha=0.8, mec='k', ms=15)
# Add colorbar
sc = []
sc.append(plt.scatter([0,0], [0,0], c=[0., 1.], s=0.1,
vmin=0, vmax=maxDist, cmap=cmap))
cbar = plt.colorbar(sc[-1])
# Add labels
plt.xlabel('Micrometers', fontsize=20)
plt.xlim([100, 550])
plt.ylabel('Micrometers', fontsize=20)
plt.ylim([0, 725])
plt.title('Heatmap of Tips Ordered by Path Length', fontsize=20)
cbar.set_label("Path Length (um)", fontsize=20)
# Does bpts truly contain all nodes? If so, returns true.
def bptsCheck(geof, bptsf):
bptsLen = [len(n) for n in bptsf]
gbLen = [len(b.nodes) for b in geof.branches]
return sum(bptsLen) == sum(gbLen)
Out[208]:
In [217]:
def pathPlot(geo):
# Find the tips and their ends
tips, ends = geo.getTips()
# Calculate path distance to each tip
pDF = PathDistanceFinder(geo, geo.soma)
pdists = [pDF.distanceTo(seg) for seg in tips]
# Build skeleton
plt.figure(figsize=(16,18))
bpts = []
for b in geo.branches:
bpts.append([[n.x, n.y] for n in b.nodes])
for b in bpts:
for c in range(len(b)-1):
plt.plot([b[c][0], b[c+1][0]],
[b[c][1], b[c+1][1]], color='k', alpha=0.9)
# Plot points colored by path distance
maxDist = max(pdists)
tfloats = [float(i)/maxDist for i in pdists]
tcolors = plt.cm.jet(tfloats)
cmap = plt.cm.jet
for t in range(len(coords)):
plt.plot(coords[t][0], coords[t][1], 'o', color=tcolors[t],
alpha=0.8, mec='k', ms=15)
# Add colorbar
sc = []
sc.append(plt.scatter([0,0], [0,0], c=[0., 1.], s=0.1,
vmin=0, vmax=maxDist, cmap=cmap))
cbar = plt.colorbar(sc[-1])
# Add labels
plt.xlabel('Micrometers', fontsize=20)
plt.xlim([100, 550])
plt.ylabel('Micrometers', fontsize=20)
plt.ylim([0, 725])
plt.title('Heatmap of Tips Ordered by Path Length', fontsize=20)
cbar.set_label("Path Length (um)", fontsize=20)
# Does bpts truly contain all nodes? If so, returns true.
bptsLen = [len(n) for n in bpts]
gbLen = [len(b.nodes) for b in geo.branches]
check = sum(bptsLen) == sum(gbLen)
print("{}, there are {} nodes in bpts and {} nodes in geo.branches[i].".format(check, sum(bptsLen), sum(gbLen)))
pathPlot(geo)