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morphometry_properties.py
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morphometry_properties.py
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import svcco
import numpy as np
import matplotlib.pyplot as plt
import pyvista as pv
q = 4
resolution = 120
cu = pv.Cube(x_length=3.72,y_length=3.72,z_length=3.72).triangulate().subdivide(5)
cube = svcco.surface()
cube.set_data(cu.points,cu.point_normals)
cube.solve()
cube.build(q=q,resolution=resolution)
print('cube constructed')
heart = svcco.surface()
heart_points = np.genfromtxt('D:\\svcco\\svcco\\implicit\\tests\\heart_points_unique.csv',delimiter=',')
heart_normals = np.genfromtxt('D:\\svcco\\svcco\\implicit\\tests\\heart_normals_unique.csv',delimiter=',')
heart.set_data(heart_points,heart_normals)
heart.solve()
heart.build(q=q,resolution=resolution,buffer=5)
print('heart constructed')
disk = pv.Disc(inner=2.8,outer=4,r_res=20,c_res=100)
cyl = disk.extrude([0,0,2],capping=True).triangulate()
cyl = svcco.utils.remeshing.remesh.remesh_surface(cyl)
cyl = cyl.subdivide(2)
cyl = svcco.utils.remeshing.remesh.remesh_surface(cyl,hausd=0.005)
cyl = cyl.compute_normals(auto_orient_normals=True,feature_angle=90)
cylinder = svcco.surface()
cylinder.set_data(cyl.points,cyl.point_normals)
cylinder.solve()
cylinder.build(q=q,resolution=resolution)
print('cylinder constructed')
left_gyrus = "D:\\Tree\\Tree_8-0\\brain_testing\\FJ3801_BP58201_FMA72658_Left inferior frontal gyrus.obj"
gyrus_no_scale = pv.read(left_gyrus)
sf = (heart.volume/gyrus_no_scale.volume)**(1/3)
gyrus_scaled = gyrus_no_scale.scale([sf,sf,sf])
left_gyrus_scaled = "left_gyrus_scaled.vtp"
gyrus_scaled.save(left_gyrus_scaled)
gyrus = svcco.surface()
gyrus.load(left_gyrus_scaled)
gyrus.solve()
gyrus.build(q=q,resolution=resolution,buffer=5)
print('gyrus constructed')
###########################################
# Test function
###########################################
def test(surf_object,size=1000):
t = svcco.tree()
t.set_boundary(surf_object)
delaunay = surf_object.pv_polydata.delaunay_3d()
convexity = surf_object.volume/delaunay.volume
if convexity > 0.95:
t.convex = True
t.set_root()
t.n_add(size)
w = svcco.utils.fluid_analysis.wss.wss(t)
r = t.data[:,21]*10 # in millimeters
l = t.data[:,20]*10 # in millimeters
a = []
for i in range(t.data.shape[0]):
if t.data[i,15] > 0:
dau_1 = int(t.data[i,15].item())
dot = np.dot(t.data[i,12:15],t.data[dau_1,12:15].T)
dot = dot/(np.linalg.norm(t.data[i,12:15])*np.linalg.norm(t.data[dau_1,12:15]))
if dot > 1:
dot = 1
ang_1 = 180*(np.arccos(dot)/np.pi)
a.append(ang_1)
if t.data[i,16] > 0:
dau_2 = int(t.data[i,16].item())
dot = np.dot(t.data[i,12:15],t.data[dau_2,12:15].T)
dot = dot/(np.linalg.norm(t.data[i,12:15])*np.linalg.norm(t.data[dau_2,12:15]))
if dot > 1:
dot = 1
ang_2 = 180*(np.arccos(dot)/np.pi)
a.append(ang_2)
a = np.array(a)
branches = svcco.sv_interface.get_sv_data.get_branches(t.data)
torts = []
for branch in branches:
tort = 0
for idx in range(len(branch)-1):
dot = np.dot(t.data[branch[idx],12:15],t.data[branch[idx+1],12:15].T)
dot = dot/(np.linalg.norm(t.data[branch[idx],12:15])*np.linalg.norm(t.data[branch[idx+1],12:15]))
if dot > 1:
dot = 1
ang = 180*(np.arccos(dot)/np.pi)
tort += ang
torts.append(tort)
torts = np.array(torts)
return w,r,l,a,torts
def test_heart(size=1000):
t = svcco.tree()
t.set_boundary(heart)
t.set_root()
t.n_add(size)
w = svcco.utils.fluid_analysis.wss.wss(t)
r = t.data[:,21]*10 #should be microns
l = t.data[:,20]*10
a = []
for i in range(t.data.shape[0]):
if t.data[i,15] > 0:
dau_1 = int(t.data[i,15].item())
dot = np.dot(t.data[i,12:15],t.data[dau_1,12:15].T)
dot = dot/(np.linalg.norm(t.data[i,12:15])*np.linalg.norm(t.data[dau_1,12:15]))
if dot > 1:
dot = 1
ang_1 = 180*(np.arccos(dot)/np.pi)
a.append(ang_1)
if t.data[i,16] > 0:
dau_2 = int(t.data[i,16].item())
dot = np.dot(t.data[i,12:15],t.data[dau_2,12:15].T)
dot = dot/(np.linalg.norm(t.data[i,12:15])*np.linalg.norm(t.data[dau_2,12:15]))
if dot > 1:
dot = 1
ang_2 = 180*(np.arccos(dot)/np.pi)
a.append(ang_2)
a = np.array(a)
branches = svcco.sv_interface.get_sv_data.get_branches(t.data)
torts = []
for branch in branches:
tort = 0
for idx in range(len(branch)-1):
dot = np.dot(t.data[branch[idx],12:15],t.data[branch[idx+1],12:15].T)
dot = dot/(np.linalg.norm(t.data[branch[idx],12:15])*np.linalg.norm(t.data[branch[idx+1],12:15]))
if dot > 1:
dot = 1
ang = 180*(np.arccos(dot)/np.pi)
tort += ang
torts.append(tort)
torts = np.array(torts)
return w,r,l,a,torts
def test_sphere(size=1000):
t = svcco.tree()
t.set_boundary(sph)
t.convex = True
t.set_root()
t.n_add(size)
w = svcco.utils.fluid_analysis.wss.wss(t)
r = t.data[:,21]*10 #should be microns
l = t.data[:,20]*10
a = []
for i in range(t.data.shape[0]):
if t.data[i,15] > 0:
dau_1 = int(t.data[i,15].item())
dot = np.dot(t.data[i,12:15],t.data[dau_1,12:15].T)
dot = dot/(np.linalg.norm(t.data[i,12:15])*np.linalg.norm(t.data[dau_1,12:15]))
if dot > 1:
dot = 1
ang_1 = 180*(np.arccos(dot)/np.pi)
a.append(ang_1)
if t.data[i,16] > 0:
dau_2 = int(t.data[i,16].item())
dot = np.dot(t.data[i,12:15],t.data[dau_2,12:15].T)
dot = dot/(np.linalg.norm(t.data[i,12:15])*np.linalg.norm(t.data[dau_2,12:15]))
if dot > 1:
dot = 1
ang_2 = 180*(np.arccos(dot)/np.pi)
a.append(ang_2)
a = np.array(a)
branches = svcco.sv_interface.get_sv_data.get_branches(t.data)
torts = []
for branch in branches:
tort = 0
for idx in range(len(branch)-1):
dot = np.dot(t.data[branch[idx],12:15],t.data[branch[idx+1],12:15].T)
dot = dot/(np.linalg.norm(t.data[branch[idx],12:15])*np.linalg.norm(t.data[branch[idx+1],12:15]))
if dot > 1:
dot = 1
ang = 180*(np.arccos(dot)/np.pi)
tort += ang
torts.append(tort)
torts = np.array(torts)
return w,r,l,a,torts
def test_gyrus(size=1000):
t = svcco.tree()
t.set_boundary(gyrus)
t.set_root()
t.n_add(size)
w = svcco.utils.fluid_analysis.wss.wss(t)
r = t.data[:,21]*10 #should be microns
l = t.data[:,20]*10
a = []
for i in range(t.data.shape[0]):
if t.data[i,15] > 0:
dau_1 = int(t.data[i,15].item())
dot = np.dot(t.data[i,12:15],t.data[dau_1,12:15].T)
dot = dot/(np.linalg.norm(t.data[i,12:15])*np.linalg.norm(t.data[dau_1,12:15]))
if dot > 1:
dot = 1
ang_1 = 180*(np.arccos(dot)/np.pi)
a.append(ang_1)
if t.data[i,16] > 0:
dau_2 = int(t.data[i,16].item())
dot = np.dot(t.data[i,12:15],t.data[dau_2,12:15].T)
dot = dot/(np.linalg.norm(t.data[i,12:15])*np.linalg.norm(t.data[dau_2,12:15]))
if dot > 1:
dot = 1
ang_2 = 180*(np.arccos(dot)/np.pi)
a.append(ang_2)
a = np.array(a)
branches = svcco.sv_interface.get_sv_data.get_branches(t.data)
torts = []
for branch in branches:
tort = 0
for idx in range(len(branch)-1):
dot = np.dot(t.data[branch[idx],12:15],t.data[branch[idx+1],12:15].T)
dot = dot/(np.linalg.norm(t.data[branch[idx],12:15])*np.linalg.norm(t.data[branch[idx+1],12:15]))
if dot > 1:
dot = 1
ang = 180*(np.arccos(dot)/np.pi)
tort += ang
torts.append(tort)
torts = np.array(torts)
return w,r,l,a,torts
def results(test_list,size=1000,iter=10,bins=50,range_w=(0,1500),range_r=(0,1),range_l=(0,4),range_a = (0,90),range_t=(0,4*180)):
WSS_ALL = []
RAD_ALL = []
LEN_ALL = []
ANG_ALL = []
TORT_ALL = []
WSS_ALL_MEAN = []
RAD_ALL_MEAN = []
LEN_ALL_MEAN = []
ANG_ALL_MEAN = []
TORT_ALL_MEAN = []
WSS_ALL_STD = []
RAD_ALL_STD = []
LEN_ALL_STD = []
ANG_ALL_STD = []
TORT_ALL_STD = []
WSS_WIDTH_ALL= []
RAD_WIDTH_ALL= []
LEN_WIDTH_ALL= []
ANG_WIDTH_ALL= []
TORT_WIDTH_ALL=[]
WSS_CENTER_ALL = []
RAD_CENTER_ALL = []
LEN_CENTER_ALL = []
ANG_CENTER_ALL = []
TORT_CENTER_ALL = []
for i in range(len(test_list)):
WSS = []
RAD = []
LEN = []
ANG = []
TORT = []
for j in range(iter):
w,r,l,a,torts = test(test_list[i],size=size)
freq_w,edges_w = np.histogram(w,bins=bins,range=range_w)
freq_r,edges_r = np.histogram(r,bins=bins,range=range_r)
freq_l,edges_l = np.histogram(l,bins=bins,range=range_l)
freq_a,edges_a = np.histogram(a,bins=bins,range=range_a)
freq_t,edges_t = np.histogram(torts,bins=bins,range=range_t)
WSS.append(freq_w.tolist())
RAD.append(freq_r.tolist())
LEN.append(freq_l.tolist())
ANG.append(freq_a.tolist())
TORT.append(freq_t.tolist())
WSS_ALL.append(WSS)
RAD_ALL.append(RAD)
LEN_ALL.append(LEN)
ANG_ALL.append(ANG)
TORT_ALL.append(TORT)
WSS = np.array(WSS)
RAD = np.array(RAD)
LEN = np.array(LEN)
ANG = np.array(ANG)
#ANG = ANG[np.isfinite(ANG)]
TORT = np.array(TORT)
#TORT = TORT[np.isfinite(TORT)]
WSS_mean = np.mean(WSS,axis=0)
WSS_ALL_MEAN.append(WSS_mean)
WSS_std = np.std(WSS,axis=0)
WSS_ALL_STD.append(WSS_std)
RAD_mean = np.mean(RAD,axis=0)
RAD_ALL_MEAN.append(RAD_mean)
RAD_std = np.std(RAD,axis=0)
RAD_ALL_STD.append(RAD_std)
LEN_mean = np.mean(LEN,axis=0)
LEN_ALL_MEAN.append(LEN_mean)
LEN_std = np.std(LEN,axis=0)
LEN_ALL_STD.append(LEN_std)
ANG_mean = np.mean(ANG,axis=0)
ANG_ALL_MEAN.append(ANG_mean)
ANG_std = np.std(ANG,axis=0)
ANG_ALL_STD.append(ANG_std)
TORT_mean = np.mean(TORT,axis=0)
TORT_ALL_MEAN.append(TORT_mean)
TORT_std = np.std(TORT,axis=0)
TORT_ALL_STD.append(TORT_std)
width_w = (edges_w[1]-edges_w[0])
WSS_WIDTH_ALL.append(width_w)
width_r = (edges_r[1]-edges_r[0])
RAD_WIDTH_ALL.append(width_r)
width_l = (edges_l[1]-edges_l[0])
LEN_WIDTH_ALL.append(width_l)
width_a = (edges_a[1]-edges_a[0])
ANG_WIDTH_ALL.append(width_a)
width_t = (edges_t[1]-edges_t[0])
TORT_WIDTH_ALL.append(width_t)
centers_w = 0.5*(edges_w[1:]+edges_w[:-1])
WSS_CENTER_ALL.append(centers_w)
centers_r = 0.5*(edges_r[1:]+edges_r[:-1])
RAD_CENTER_ALL.append(centers_r)
centers_l = 0.5*(edges_l[1:]+edges_l[:-1])
LEN_CENTER_ALL.append(centers_l)
centers_a = 0.5*(edges_a[1:]+edges_a[:-1])
ANG_CENTER_ALL.append(centers_a)
centers_t = 0.5*(edges_t[1:]+edges_t[:-1])
TORT_CENTER_ALL.append(centers_t)
fig,ax = plt.subplots(4,len(test_list))
for i in range(len(test_list)):
centers_w = WSS_CENTER_ALL[i]
WSS_mean = WSS_ALL_MEAN[i]
width_w = WSS_WIDTH_ALL[i]
WSS_std = WSS_ALL_STD[i]
centers_r = RAD_CENTER_ALL[i]
RAD_mean = RAD_ALL_MEAN[i]
width_r = RAD_WIDTH_ALL[i]
RAD_std = RAD_ALL_STD[i]
centers_l = LEN_CENTER_ALL[i]
LEN_mean = LEN_ALL_MEAN[i]
width_l = LEN_WIDTH_ALL[i]
LEN_std = LEN_ALL_STD[i]
centers_a = ANG_CENTER_ALL[i]
ANG_mean = ANG_ALL_MEAN[i]
width_a = ANG_WIDTH_ALL[i]
ANG_std = ANG_ALL_STD[i]
centers_t = TORT_CENTER_ALL[i]
TORT_mean = TORT_ALL_MEAN[i]
width_t = TORT_WIDTH_ALL[i]
TORT_std = TORT_ALL_STD[i]
#ax[0][0].bar(centers_w,WSS_mean,width=width_w,yerr=WSS_std)
if len(test_list) > 1:
ax[0][i].bar(centers_r,RAD_mean,width=width_r,yerr=RAD_std)
ax[1][i].bar(centers_l,LEN_mean,width=width_l,yerr=LEN_std)
ax[2][i].bar(centers_a,ANG_mean,width=width_a,yerr=ANG_std)
ax[3][i].bar(centers_t,TORT_mean,width=width_t,yerr=TORT_std)
#for pos,y,err in zip(centers_w,WSS_mean,WSS_std):
# ax[0][0].errorbar(pos,y,err,capsize=2,color='black')
for pos,y,err in zip(centers_r,RAD_mean,RAD_std):
ax[0][i].errorbar(pos,y,err,capsize=2,color='black')
for pos,y,err in zip(centers_l,LEN_mean,LEN_std):
ax[1][i].errorbar(pos,y,err,capsize=2,color='black')
for pos,y,err in zip(centers_a,ANG_mean,ANG_std):
ax[2][i].errorbar(pos,y,err,capsize=2,color='black')
for pos,y,err in zip(centers_t,TORT_mean,TORT_std):
ax[3][i].errorbar(pos,y,err,capsize=2,color='black')
ax[0][i].set_xlim([0,None])
ax[0][i].set_ylim([0,500])
ax[1][i].set_xlim([0,None])
ax[1][i].set_ylim([0,100])
ax[2][i].set_xlim([0,90])
ax[2][i].set_ylim([0,125])
ax[3][i].set_xlim([0,None])
ax[3][i].set_ylim([0,350])
else:
ax[0].bar(centers_r,RAD_mean,width=width_r,yerr=RAD_std)
ax[1].bar(centers_l,LEN_mean,width=width_l,yerr=LEN_std)
ax[2].bar(centers_a,ANG_mean,width=width_a,yerr=ANG_std)
ax[3].bar(centers_t,TORT_mean,width=width_t,yerr=TORT_std)
#for pos,y,err in zip(centers_w,WSS_mean,WSS_std):
# ax[0][0].errorbar(pos,y,err,capsize=2,color='black')
for pos,y,err in zip(centers_r,RAD_mean,RAD_std):
ax[0].errorbar(pos,y,err,capsize=2,color='black')
for pos,y,err in zip(centers_l,LEN_mean,LEN_std):
ax[1].errorbar(pos,y,err,capsize=2,color='black')
for pos,y,err in zip(centers_a,ANG_mean,ANG_std):
ax[2].errorbar(pos,y,err,capsize=2,color='black')
for pos,y,err in zip(centers_t,TORT_mean,TORT_std):
ax[3].errorbar(pos,y,err,capsize=2,color='black')
ax[0].set_xlim([0,None])
ax[0].set_ylim([0,None])
ax[1].set_xlim([0,None])
ax[1].set_ylim([0,None])
ax[2].set_xlim([0,90])
ax[2].set_ylim([0,None])
ax[3].set_xlim([0,None])
ax[3].set_ylim([0,None])
if len(test_list) > 1:
ax[0][0].set_ylabel('Number of Vessels')
ax[1][0].set_ylabel('Number of Vessels')
ax[2][0].set_ylabel('Number of Vessels')
ax[3][0].set_ylabel('Number of Vessels')
for i in range(len(test_list)):
ax[0][i].set_xlabel('Vessel Radius (mm)')
ax[1][i].set_xlabel('Vessel Length (mm)')
ax[2][i].set_xlabel('Parent-daughter Angles')
ax[3][i].set_xlabel('Tortuosity')
else:
ax[0].set_ylabel('Number of Vessels')
ax[1].set_ylabel('Number of Vessels')
ax[2].set_ylabel('Number of Vessels')
ax[3].set_ylabel('Number of Vessels')
ax[0].set_xlabel('Vessel Radius (mm)')
ax[1].set_xlabel('Vessel Length (mm)')
ax[2].set_xlabel('Parent-daughter Angles')
ax[3].set_xlabel('Tortuosity')
fig.savefig('morphometry_surfaces-{}_num_vessels-{}_iter-{}_num_bins-{}.svg'.format(len(test_list),size,iter,bins),format='svg')
return fig,ax
f,a = results([cube,cylinder,heart,gyrus])