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result_analysis.py
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result_analysis.py
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import os
import csv
import glob
import heapq
import numpy as np
import vtk
from vtk.util.numpy_support import vtk_to_numpy
import pandas as pd
import matplotlib
# matplotlib.use('Agg')
matplotlib.use("TkAgg")
import matplotlib.pyplot as plt
plt.style.use('ggplot')
import math
import json
from tqdm import tqdm
from scipy.interpolate import interp1d
from scipy.optimize import curve_fit
def readCSV(path):
f = open(path, 'rb')
reader = csv.reader(f)
headers = reader.next()
column = {}
for h in headers:
column[h] = []
for row in reader:
for h, v in zip(headers, row):
if v == "-1.#IND":
v = 0
column[h].append(float(v))
return column
def CSVwrite(CSVpath,row):
csvfile = open(CSVpath,'a')
csv_writer = csv.writer(csvfile)
csv_writer.writerow(row)
csvfile.close()
def vtkArrayMin(vtkArray):
arrayMax = vtkArray.GetDataTypeMax()
idx = 0
for i in range(vtkArray.GetNumberOfTuples()):
if vtkArray.GetTuple(i)[0] < arrayMax:
arrayMax = vtkArray.GetTuple(i)[0]
idx = i
return idx
def vtkArrayMax(vtkArray):
arrayMin = vtkArray.GetDataTypeMin()
idx = 0
for i in range(vtkArray.GetNumberOfTuples()):
if vtkArray.GetTuple(i)[0] > arrayMin:
arrayMin = vtkArray.GetTuple(i)[0]
idx = i
return idx
class MyInteractorStyle(vtk.vtkInteractorStyleTrackballCamera):
def __init__(self,parent=None):
# intractorStyleTrackballCamera does not inherit from render window interactor, nned to port manually
# self.parent = self.GetInteractor()
self.parent = vtk.vtkRenderWindowInteractor()
if(parent is not None):
self.parent = parent
# self.AddObserver("LeftButtonPressEvent",self.leftButtonPressEvent)
self.AddObserver("KeyPressEvent",self.keyPressEvent)
self.ActiveSelection = 0 # 0 refers to min point, 1 refers to max point
# self.LastPickedActor = None
# self.LastPickedProperty = vtk.vtkProperty()
self.minSphereSource = None
self.maxSphereSource = None
self.centerline = None
self.textActor = None
def SetMinSphereSource(self,minSphereSource):
self.minSphereSource = minSphereSource
def SetMaxSphereSource(self,maxSphereSource):
self.maxSphereSource = maxSphereSource
def SetCenterline(self,centerline):
self.centerline = centerline
def SetSelectionTextActor(self,textActor):
self.textActor = textActor
def keyPressEvent(self,obj,event):
key = self.parent.GetKeySym()
if key == 'Tab':
if self.ActiveSelection == 0:
self.ActiveSelection = 1
self.textActor.SetInput("Current selection: max point (green)")
print("To locate max point")
self.parent.GetRenderWindow().Render()
else:
self.ActiveSelection = 0
self.textActor.SetInput("Current selection: min point (red)")
print("To locate min point")
self.parent.GetRenderWindow().Render()
if key == "space":
self.parent.GetPicker().Pick(self.parent.GetEventPosition()[0],
self.parent.GetEventPosition()[1],
0, # always zero.
self.parent.GetRenderWindow().GetRenderers().GetFirstRenderer())
picked = self.parent.GetPicker().GetPickPosition();
# Create kd tree
kDTree = vtk.vtkKdTreePointLocator()
kDTree.SetDataSet(self.centerline)
kDTree.BuildLocator()
# Find the closest points to picked point
iD = kDTree.FindClosestPoint(picked)
# Get the coordinates of the closest point
closestPoint = kDTree.GetDataSet().GetPoint(iD)
if self.ActiveSelection == 0:
self.minSphereSource.SetCenter(closestPoint)
else:
self.maxSphereSource.SetCenter(closestPoint)
self.parent.GetRenderWindow().Render()
if key == "Return":
# Close the window
self.parent.GetRenderWindow().Finalize()
# Stop the interactor
self.parent.TerminateApp()
return
def fit_func(x, a, b, c):
return a * b*(x+c) * np.exp(-b * (x+c)**2)
def plot_centerline_result(centerline, array_names, result_path, dev_result_path ,minPoint=(0,0,0),bifurcationPoint=(0,0,0)):
thresholdFilter = vtk.vtkThreshold()
thresholdFilter.ThresholdBetween(1,9999)
thresholdFilter.SetInputData(centerline)
thresholdFilter.SetInputArrayToProcess(0, 0, 0, vtk.vtkDataObject.FIELD_ASSOCIATION_CELLS, "CenterlineIds_average")
thresholdFilter.Update()
x = vtk_to_numpy(thresholdFilter.GetOutput().GetPointData().GetArray("Abscissas_average"))
x = [(value - x[0]) for value in x]
kDTree = vtk.vtkKdTreePointLocator()
kDTree.SetDataSet(centerline)
kDTree.BuildLocator()
# get the abscissas of bifurcation point
if bifurcationPoint != (0,0,0):
iD = kDTree.FindClosestPoint(bifurcationPoint)
# Get the id of the closest point
bifPointAbscissas = kDTree.GetDataSet().GetPointData().GetArray("Abscissas_average").GetTuple(iD)[0] - \
vtk_to_numpy(thresholdFilter.GetOutput().GetPointData().GetArray("Abscissas_average"))[0]
# get the abscissas of min point
if minPoint != (0,0,0):
iD = kDTree.FindClosestPoint(minPoint)
# Get the id of the closest point
minPointAbscissas = kDTree.GetDataSet().GetPointData().GetArray("Abscissas_average").GetTuple(iD)[0] - \
vtk_to_numpy(thresholdFilter.GetOutput().GetPointData().GetArray("Abscissas_average"))[0]
fig, axs = plt.subplots(len(array_names),1)
fig.suptitle("CFD result")
fig.set_size_inches(10,8)
fig2, axs2 = plt.subplots(len(array_names),1)
fig2.suptitle("CFD result derivatives")
fig2.set_size_inches(10,8)
fit_dict= {}
for i in range(len(array_names)):
popt_list = []
for lineId in range(int(centerline.GetCellData().GetArray("CenterlineIds_average").GetMaxNorm())):
thresholdFilter.ThresholdBetween(lineId,lineId)
thresholdFilter.Update()
x = vtk_to_numpy(thresholdFilter.GetOutput().GetPointData().GetArray("Abscissas_average"))
x = [(value - x[0]) for value in x]
if len(x) < 3:
continue
y = vtk_to_numpy(thresholdFilter.GetOutput().GetPointData().GetArray(array_names[i]))
if len(y.shape) > 1:
if y.shape[1] == 3:
y = [math.sqrt(value[0]**2 + value[1]**2 +value[2]**2 ) for value in y]
if len(array_names) == 1:
ax = axs
ax2 = axs2
else:
ax = axs[i]
ax2 = axs2[i]
order = np.argsort(x)
xs = np.array(x)[order]
ys = np.array(y)[order]
unique, index = np.unique(xs, axis=-1, return_index=True)
xs = xs[index]
ys = ys[index]
f = interp1d(xs,ys,kind="cubic")
xs = np.linspace(0, np.amax(x), num=200, endpoint=True)
ys = f(xs)
dys = np.gradient(ys, xs)
ax.plot(xs,ys)
ax2.plot(xs,dys)
# curve fitting on specific values
if array_names[i] == "Radius_average" or array_names[i] == "U_average" or array_names[i] == "p(mmHg)_average":
try:
popt, pcov = curve_fit(fit_func, xs, dys, p0=[50,0.1,-50])
yfit = fit_func(xs, *popt)
ax2.plot(xs, fit_func(xs, *popt), 'k-.',label='fit: a=%5.3f, b=%5.3f, c=%5.3f' % tuple(popt))
except RuntimeError:
popt = [0,0,0]
popt_list.append(popt)
if array_names[i] == "Radius_average":
ylabel = "Radius (mm)"
ymin=0
ymax = 5
ymin2=-5
ymax2 = 5
elif array_names[i] == "U_average":
ylabel = "Velocity (ms^-1)"
ymin=0
ymax = 3
ymin2=-3
ymax2 = 3
elif array_names[i] == "p(mmHg)_average":
ylabel = "Pressure (mmHg)"
ymin=0
ymax = 180
ymin2=-180
ymax2 = 180
elif array_names[i] == "vorticity_average":
ylabel = "Vorticity (s^-1)"
ymin=0
ymax = 4000
ymin2=-4000
ymax2 = 4000
elif array_names[i] == "Curvature_average":
ylabel = "Curvature"
ymin = -1.5
ymax = 1.5
ymin2 = -1.5
ymax2 = 1.5
elif array_names[i] == "Torsion_average":
ylabel = "Torsion"
ymin = -100000
ymax = 100000
ymin2 = -100000
ymax2 = 100000
if bifurcationPoint !=(0,0,0):
ax.axvline(x=bifPointAbscissas,ymin=ymin,ymax=ymax,linestyle ="--",color='m')
ax2.axvline(x=bifPointAbscissas,ymin=ymin,ymax=ymax,linestyle ="--",color='m')
if minPoint !=(0,0,0):
ax.axvline(x=minPointAbscissas,ymin=ymin,ymax=ymax,linestyle ="--",color='c')
ax2.axvline(x=minPointAbscissas,ymin=ymin,ymax=ymax,linestyle ="--",color='c')
ax.set_ylabel(ylabel)
ax2.set_ylabel(ylabel)
if i == (len(array_names)-1):
ax.set_xlabel("Abscissas (mm)")
ax2.set_xlabel("Abscissas (mm)")
else:
ax.set_xticklabels([])
ax2.set_xticklabels([])
x = vtk_to_numpy(centerline.GetPointData().GetArray("Abscissas_average"))
ax.set_xlim(x[0],x[-1])
ax.set_ylim(ymin,ymax)
ax2.set_xlim(x[0],x[-1])
ax2.set_ylim(ymin2,ymax2)
# max of popt[0]
if len(popt_list) > 0:
fit_dict.update({array_names[i]: np.abs(np.amax(np.array(popt_list),axis=0)[0])})
# save the plot
fig.savefig(result_path,dpi=100)
fig.clf()
fig2.savefig(dev_result_path,dpi=100)
fig2.clf()
plt.close("all")
return fit_dict
def centerline_probe_result(centerline_file,vtk_file_list, output_dir,minPoint=(0,0,0), bifurcationPoint=(0,0,0)):
# read centerline
centerlineReader = vtk.vtkXMLPolyDataReader()
centerlineReader.SetFileName(centerline_file)
centerlineReader.Update()
centerline = centerlineReader.GetOutput()
# read vtk files
vtk_files = []
centerlines = []
if not os.path.exists(output_dir):
os.makedirs(output_dir)
if not os.path.exists(os.path.join(output_dir,"centerlines")):
os.makedirs(os.path.join(output_dir,"centerlines"))
# average filter
averageFilter = vtk.vtkTemporalStatistics()
for file_name in vtk_file_list:
reader = vtk.vtkUnstructuredGridReader()
reader.SetFileName(file_name)
reader.Update()
geomFilter = vtk.vtkGeometryFilter()
geomFilter.SetInputData(reader.GetOutput())
geomFilter.Update()
# scale up the CFD result
transform = vtk.vtkTransform()
transform.Scale(1000,1000,1000)
# transformFilter = vtk.vtkTransformPolyDataFilter()
transformFilter = vtk.vtkTransformFilter()
transformFilter.SetInputData(geomFilter.GetOutput())
transformFilter.SetTransform(transform)
transformFilter.Update()
vtk_files.append(transformFilter.GetOutput())
interpolator = vtk.vtkPointInterpolator()
interpolator.SetSourceData(transformFilter.GetOutput())
interpolator.SetInputData(centerline)
interpolator.Update()
# convert to desired unit
# get the first element pressure
try:
first_point_pressure = interpolator.GetOutput().GetPointData().GetArray("p").GetValue(0)
except:
first_point_pressure = 120
converter = vtk.vtkArrayCalculator()
converter.SetInputData(interpolator.GetOutput())
converter.AddScalarArrayName("p")
converter.SetFunction("120 + (p - {}) * 921 * 0.0075".format(first_point_pressure)) # 921 = mu/nu = density of blood, 0.0075 converts from Pascal to mmHg, offset 120mmHg at ica
converter.SetResultArrayName("p(mmHg)")
converter.Update()
# output the probe centerline
centerline_output_path = os.path.join(
os.path.dirname(centerline_file),
output_dir,
"centerlines",
"centerline_probe_{}.vtp".format(os.path.split(file_name)[1].split("_")[1].split(".")[0])
)
centerlines.append(converter.GetOutput())
averageFilter.SetInputData(converter.GetOutput())
averageFilter.Update()
writer = vtk.vtkXMLPolyDataWriter()
writer.SetInputData(converter.GetOutput())
writer.SetFileName(centerline_output_path)
writer.Update()
centerline_output_path = os.path.join(
os.path.dirname(centerline_file),
output_dir,
"centerlines",
"centerline_probe_avg.vtp"
)
writer = vtk.vtkXMLPolyDataWriter()
writer.SetInputData(averageFilter.GetOutput())
writer.SetFileName(centerline_output_path)
writer.Update()
# plot result
plot_result_path = os.path.join(os.path.dirname(centerline_file),output_dir,"result.png")
dev_plot_result_path = os.path.join(os.path.dirname(centerline_file),output_dir,"result_dev.png")
plot_array = [
"Radius_average",
"U_average",
"p(mmHg)_average",
"vorticity_average",
"Curvature_average",
"Torsion_average"
]
fit_dict = plot_centerline_result(
averageFilter.GetOutput(),
["U_average","p(mmHg)_average"],
#["Radius_average","U_average","p(mmHg)_average","vorticity_average","Curvature_average","Torsion_average"],
plot_array,
plot_result_path,
dev_plot_result_path,
minPoint = minPoint,
bifurcationPoint = bifurcationPoint)
# extract ica
thresholdFilter = vtk.vtkThreshold()
thresholdFilter.ThresholdBetween(0,999)
thresholdFilter.SetInputData(averageFilter.GetOutput())
thresholdFilter.SetInputArrayToProcess(0, 0, 0, vtk.vtkDataObject.FIELD_ASSOCIATION_CELLS, "CenterlineIds_average")
thresholdFilter.Update()
if thresholdFilter.GetOutput().GetNumberOfPoints() == 0 or fit_dict == None:
tqdm.write("Centerline file {} does not contain suitable number of CenterlineIds".format(centerline_file))
return_value = {
'radius mean(mm)': "NA",
'max radius gradient':"NA",
'radius min(mm)': "NA",
'pressure mean(mmHg)': "NA",
'max pressure gradient(mmHg)': "NA",
'in/out pressure gradient(mmHg)': "NA",
'velocity mean(ms^-1)': "NA",
'max velocity gradient(ms^-1)': "NA",
'peak velocity(ms^-1)': "NA",
'vorticity mean(s^-1)': "NA",
'peak vorticity(s^-1)': "NA"
}
else:
# compute result values
abscissas = vtk_to_numpy(thresholdFilter.GetOutput().GetPointData().GetArray("Abscissas_average"))
radius = vtk_to_numpy(thresholdFilter.GetOutput().GetPointData().GetArray("Radius_average"))
pressure = vtk_to_numpy(thresholdFilter.GetOutput().GetPointData().GetArray("p(mmHg)_average"))
pressure_gradient = np.diff(pressure)/np.diff(abscissas)
velocity = vtk_to_numpy(thresholdFilter.GetOutput().GetPointData().GetArray("U_average"))
velocity = [math.sqrt(value[0]**2 + value[1]**2 +value[2]**2 ) for value in velocity]
vorticity = vtk_to_numpy(thresholdFilter.GetOutput().GetPointData().GetArray("vorticity_average"))
vorticity = [math.sqrt(value[0]**2 + value[1]**2 +value[2]**2 ) for value in vorticity]
return_value = {
'radius mean(mm)': np.mean(radius),
'radius min(mm)': np.min(radius),
'max radius gradient': fit_dict["Radius_average"],
'pressure mean(mmHg)': np.mean(pressure),
# 'max pressure gradient(mmHg)': np.mean(heapq.nlargest(5, pressure_gradient)),
'max pressure gradient(mmHg)': fit_dict["p(mmHg)_average"],
'in/out pressure gradient(mmHg)': np.mean(pressure[0:5]) - np.mean(pressure[-5:]),
'velocity mean(ms^-1)': np.mean(velocity),
'peak velocity(ms^-1)': np.mean(heapq.nlargest(5, velocity)),
'max velocity gradient(ms^-1)': fit_dict["U_average"],
'vorticity mean(s^-1)': np.mean(vorticity),
'peak vorticity(s^-1)': np.mean(heapq.nlargest(5, vorticity))
}
# moving variance matrix
mv_fields = [
"Radius_average",
"U_average",
"p(mmHg)_average",
"vorticity_average",
"Curvature_average",
"Torsion_average"
]
mv_windows = np.arange(3,10,2)
plot_result_path = os.path.join(os.path.dirname(centerline_file),output_dir,"moving_variance.png")
dev_plot_result_path = os.path.join(os.path.dirname(centerline_file),output_dir,"moving_variance_dev.png")
mv_matrix_df, mv_dy_matrix_df = moving_variance_matrix(averageFilter.GetOutput(),mv_fields,mv_windows,
minPoint = minPoint,
bifurcationPoint = bifurcationPoint,
result_path = plot_result_path,
dev_result_path=dev_plot_result_path)
return return_value, mv_matrix_df, mv_dy_matrix_df
def rolling_window(a, window):
pad = np.zeros(len(a.shape), dtype=np.int32)
pad[-1] = window-1
pad = list(zip(pad, np.zeros(len(a.shape), dtype=np.int32)))
a = np.pad(a, pad,mode='edge')
shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
strides = a.strides + (a.strides[-1],)
return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)
def moving_variance_matrix(centerline,fields, windows, minPoint=(0,0,0), bifurcationPoint=(0,0,0), result_path="", dev_result_path=""):
# create input array from centerline
thresholdFilter = vtk.vtkThreshold()
thresholdFilter.SetInputData(centerline)
thresholdFilter.SetInputArrayToProcess(0, 0, 0, vtk.vtkDataObject.FIELD_ASSOCIATION_CELLS, "CenterlineIds_average")
thresholdFilter.Update()
fig, axs = plt.subplots(len(fields),1)
fig.suptitle("CFD Moving Variance")
fig.set_size_inches(10,8)
fig2, axs2 = plt.subplots(len(fields),1)
fig2.suptitle("CFD Derivative Moving Variance")
fig2.set_size_inches(10,8)
# build kd tree to locate the nearest point
# Create kd tree
kDTree = vtk.vtkKdTreePointLocator()
kDTree.SetDataSet(centerline)
kDTree.BuildLocator()
# get the abscissas of bifurcation point
if bifurcationPoint != (0,0,0):
iD = kDTree.FindClosestPoint(bifurcationPoint)
# Get the id of the closest point
bifPointAbscissas = kDTree.GetDataSet().GetPointData().GetArray("Abscissas_average").GetTuple(iD)[0] - \
vtk_to_numpy(thresholdFilter.GetOutput().GetPointData().GetArray("Abscissas_average"))[0]
# get the abscissas of min point
if minPoint != (0,0,0):
iD = kDTree.FindClosestPoint(minPoint)
# Get the id of the closest point
minPointAbscissas = kDTree.GetDataSet().GetPointData().GetArray("Abscissas_average").GetTuple(iD)[0] - \
vtk_to_numpy(thresholdFilter.GetOutput().GetPointData().GetArray("Abscissas_average"))[0]
col_name = ["branch","window"] + fields
pmv_df = pd.DataFrame(columns=col_name)
col_name_dy = ["branch","window"] + [y+"_dev" for y in fields]
pmv_dy_df = pd.DataFrame(columns=col_name_dy)
for lineId in range(int(centerline.GetCellData().GetArray("CenterlineIds_average").GetMaxNorm())):
a_x = []
a_y = []
a_dy = []
thresholdFilter.ThresholdBetween(lineId,lineId)
thresholdFilter.Update()
# need at least 3 points to perform interpolation
if thresholdFilter.GetOutput().GetNumberOfPoints() < 4:
continue
for i in range(len(fields)):
x = vtk_to_numpy(thresholdFilter.GetOutput().GetPointData().GetArray("Abscissas_average"))
x = [(value - x[0]) for value in x]
y = vtk_to_numpy(thresholdFilter.GetOutput().GetPointData().GetArray(fields[i]))
if len(y.shape) > 1:
if y.shape[1] == 3:
y = [math.sqrt(value[0]**2 + value[1]**2 +value[2]**2 ) for value in y]
order = np.argsort(x)
xs = np.array(x)[order]
ys = np.array(y)[order]
unique, index = np.unique(xs, axis=-1, return_index=True)
xs = xs[index]
ys = ys[index]
f = interp1d(xs,ys,kind="cubic")
xnew = np.linspace(0, np.amax(x), num=50, endpoint=True)
ynew = f(xnew)
dy = np.gradient(ynew, xnew)
a_x.append(xnew)
a_y.append(ynew)
a_dy.append(dy)
a_x = np.array(a_x)
a_y = np.array(a_y)
a_dy = np.array(a_dy)
for window in windows:
mv = np.var(rolling_window(a_y, window) , axis=-1)
mv_dy = np.var(rolling_window(a_dy, window) , axis=-1)
pmv = np.amax(mv,axis=-1)
pmv = np.concatenate(([lineId,window],pmv))
pmv_df.loc[len(pmv_df)] = pmv
pmv_dy = np.amax(mv_dy,axis=-1)
pmv_dy = np.concatenate(([lineId,window],pmv_dy))
pmv_dy_df.loc[len(pmv_dy_df)] = pmv_dy
for i in range(len(fields)):
# plot moving variance
if len(fields) == 1:
ax = axs
ax2 = axs2
else:
ax = axs[i]
ax2 = axs2[i]
ax.plot(a_x[i,:],mv[i,:])
ax2.plot(a_x[i,:],mv_dy[i,:])
if fields[i] == "Radius_average":
ylabel = "Radius (mm)"
ymin = 0
ymax = 0.5
elif fields[i] == "U_average":
ylabel = "Velocity (ms^-1)"
ymin = 0
ymax = 0.5
elif fields[i] == "p(mmHg)_average":
ylabel = "Pressure (mmHg)"
ymin = 0
ymax = 1e3
elif fields[i] == "vorticity_average":
ylabel = "Vorticity (s^-1)"
ymin = 0
ymax = 1e7
elif fields[i] == "Curvature_average":
ylabel = "Curvature"
ymin = 0
ymax = 1e0
elif fields[i] == "Torsion_average":
ylabel = "Torsion"
ymin = 0
ymax = 1e9
if bifurcationPoint !=(0,0,0):
ax.axvline(x=bifPointAbscissas,ymin=0,ymax=1,linestyle="--",color='m')
ax2.axvline(x=bifPointAbscissas,ymin=0,ymax=1,linestyle="--",color='m')
if minPoint !=(0,0,0):
ax.axvline(x=minPointAbscissas,ymin=0,ymax=1,linestyle="--",color='c')
ax2.axvline(x=minPointAbscissas,ymin=0,ymax=1,linestyle="--",color='c')
ax.set_ylabel(ylabel)
ax2.set_ylabel(ylabel)
if i == (len(fields)-1):
ax.set_xlabel("Abscissas (mm)")
ax2.set_xlabel("Abscissas (mm)")
else:
ax.set_xticklabels([])
ax2.set_xticklabels([])
ax.set_xlim(x[0],x[-1])
ax.set_ylim(ymin,ymax)
ax2.set_xlim(x[0],x[-1])
ax2.set_ylim(ymin,ymax)
# save the plot
fig.savefig(result_path,dpi=100)
fig.clf()
fig2.savefig(dev_result_path,dpi=100)
fig2.clf()
plt.close("all")
pmv_df = pmv_df.groupby(['window']).max()
pmv_df = pmv_df.drop(columns=['branch'])
pmv_dy_df = pmv_dy_df.groupby(['window']).max()
pmv_dy_df = pmv_dy_df.drop(columns=['branch'])
return pmv_df, pmv_dy_df
def probe_min_max_point(centerline_filename, surface_filename, minPoint=(0,0,0), maxPoint=(0,0,0),probe=True):
centerlineReader = vtk.vtkXMLPolyDataReader()
centerlineReader.SetFileName(centerline_filename)
centerlineReader.Update()
centerline = centerlineReader.GetOutput()
centerline.GetCellData().SetScalars(centerline.GetCellData().GetArray(2));
centerline.GetPointData().SetScalars(centerline.GetPointData().GetArray("Radius"));
surfaceReader = vtk.vtkSTLReader()
surfaceReader.SetFileName(surface_filename)
surfaceReader.Update()
surface = surfaceReader.GetOutput()
lut = vtk.vtkLookupTable()
# lut.SetNumberOfTableValues(3);
lut.Build()
# Fill in a few known colors, the rest will be generated if needed
# lut.SetTableValue(0, 1.0000, 0 , 0, 1);
# lut.SetTableValue(1, 0.0000, 1.0000, 0.0000, 1);
# lut.SetTableValue(2, 0.0000, 0.0000, 1.0000, 1);
centerlineMapper = vtk.vtkPolyDataMapper()
centerlineMapper.SetInputData(centerline)
centerlineMapper.SetScalarRange(0, centerline.GetPointData().GetScalars().GetMaxNorm());
centerlineMapper.SetLookupTable(lut);
centerlineMapper.SetScalarModeToUsePointData()
surfaceMapper = vtk.vtkPolyDataMapper()
surfaceMapper.SetInputData(surface)
scalarBar = vtk.vtkScalarBarActor()
scalarBar.SetLookupTable(centerlineMapper.GetLookupTable());
scalarBar.SetTitle("Radius");
scalarBar.SetNumberOfLabels(4);
scalarBar.SetWidth(0.08)
scalarBar.SetHeight(0.6)
scalarBar.SetPosition(0.9,0.1)
# auto find the smallest radius point
radius = centerline.GetPointData().GetArray("Radius")
# build kd tree to locate the nearest point
# Create kd tree
kDTree = vtk.vtkKdTreePointLocator()
kDTree.SetDataSet(centerline)
kDTree.BuildLocator()
minSource = vtk.vtkSphereSource()
if minPoint == (0,0,0):
minIdx = vtkArrayMin(radius)
closestPoint = centerline.GetPoint(minIdx)
else:
# Find the closest point to the picked point
iD = kDTree.FindClosestPoint(minPoint)
# Get the id of the closest point
closestPoint = kDTree.GetDataSet().GetPoint(iD)
minSource.SetCenter(closestPoint)
minSource.SetRadius(0.3);
minMapper = vtk.vtkPolyDataMapper()
minMapper.SetInputConnection(minSource.GetOutputPort());
minActor = vtk.vtkActor()
minActor.SetMapper(minMapper);
minActor.GetProperty().SetColor((1.0,0.0,0.0))
maxSource = vtk.vtkSphereSource()
if maxPoint == (0,0,0):
maxIdx = vtkArrayMin(radius)
closestPoint = centerline.GetPoint(maxIdx)
else:
# Find the closest point to the picked point
iD = kDTree.FindClosestPoint(maxPoint)
# Get the id of the closest point
closestPoint = kDTree.GetDataSet().GetPoint(iD)
maxSource.SetCenter(closestPoint)
maxSource.SetRadius(0.3);
maxMapper = vtk.vtkPolyDataMapper()
maxMapper.SetInputConnection(maxSource.GetOutputPort());
maxActor = vtk.vtkActor()
maxActor.SetMapper(maxMapper);
maxActor.GetProperty().SetColor((0.0,1.0,0.0))
centerlineActor = vtk.vtkActor()
centerlineActor.SetMapper(centerlineMapper)
surfaceActor = vtk.vtkActor()
surfaceActor.SetMapper(surfaceMapper)
surfaceActor.GetProperty().SetOpacity(0.3)
# text actor
usageTextActor = vtk.vtkTextActor()
usageTextActor.GetPositionCoordinate().SetCoordinateSystemToNormalizedViewport()
usageTextActor.GetPosition2Coordinate().SetCoordinateSystemToNormalizedViewport()
usageTextActor.SetPosition([0.001, 0.05])
usageTextActor.SetInput("Tab: Switch max/min point\nSpace: Locate max/min point\nEnter/Close Window: Process")
currentSelectionTextActor = vtk.vtkTextActor()
currentSelectionTextActor.GetPositionCoordinate().SetCoordinateSystemToNormalizedViewport()
currentSelectionTextActor.GetPosition2Coordinate().SetCoordinateSystemToNormalizedViewport()
currentSelectionTextActor.SetPosition([0.25, 0.1])
currentSelectionTextActor.SetInput("Current selection: min point (red)")
if probe:
renderer = vtk.vtkRenderer()
renderer.AddActor(centerlineActor)
renderer.AddActor(surfaceActor)
renderer.AddActor(minActor)
renderer.AddActor(maxActor)
renderer.AddActor2D(scalarBar)
renderer.AddActor(usageTextActor)
renderer.AddActor(currentSelectionTextActor)
renderWindow = vtk.vtkRenderWindow()
renderWindow.AddRenderer(renderer)
renderWindowInteractor = vtk.vtkRenderWindowInteractor()
renderWindowInteractor.SetRenderWindow(renderWindow)
mystyle = MyInteractorStyle(renderWindowInteractor)
mystyle.SetMaxSphereSource(maxSource)
mystyle.SetMinSphereSource(minSource)
mystyle.SetCenterline(centerline)
mystyle.SetSelectionTextActor(currentSelectionTextActor)
renderWindowInteractor.SetInteractorStyle(mystyle)
renderWindow.SetSize(1024,780); #(width, height)
renderWindow.Render()
renderWindowInteractor.Start()
minPoint = minSource.GetCenter()
maxPoint = maxSource.GetCenter()
# compute degree of stenosis
minIdx = kDTree.FindClosestPoint(minPoint)
minRadius = centerline.GetPointData().GetArray("Radius").GetTuple(minIdx)[0]
maxIdx = kDTree.FindClosestPoint(maxPoint)
maxRadius = centerline.GetPointData().GetArray("Radius").GetTuple(maxIdx)[0]
DoS = (1-minRadius/maxRadius)*100
tqdm.write("min radius = {:.2f}\nmax radius = {:.2f} mm\nDegree of stenosis = {:.2f} %".format(minRadius,maxRadius,DoS))
return DoS, minPoint, maxPoint
def translesional_result(centerline_file, wall_file,vtk_file_list, output_dir,minPoint=(0,0,0), prox_dist=5, dist_dist=5):
# read centerline
centerlineReader = vtk.vtkXMLPolyDataReader()
centerlineReader.SetFileName(centerline_file)
centerlineReader.Update()
centerline = centerlineReader.GetOutput()
# read vessel wall
wallReader = vtk.vtkSTLReader()
wallReader.SetFileName(wall_file)
wallReader.Update()
# read vtk files
vtk_files = []
centerlines = []
if not os.path.exists(output_dir):
os.makedirs(output_dir)
if not os.path.exists(os.path.join(output_dir,"centerlines")):
os.makedirs(os.path.join(output_dir,"centerlines"))
# average filter
averageFilter = vtk.vtkTemporalStatistics()
for file_name in vtk_file_list:
reader = vtk.vtkUnstructuredGridReader()
reader.SetFileName(file_name)
reader.Update()
geomFilter = vtk.vtkGeometryFilter()
geomFilter.SetInputData(reader.GetOutput())
geomFilter.Update()
# scale up the CFD result
transform = vtk.vtkTransform()
transform.Scale(1000,1000,1000)
transformFilter = vtk.vtkTransformPolyDataFilter()
transformFilter.SetInputData(geomFilter.GetOutput())
transformFilter.SetTransform(transform)
transformFilter.Update()
vtk_files.append(transformFilter.GetOutput())
interpolator = vtk.vtkPointInterpolator()
interpolator.SetSourceData(transformFilter.GetOutput())
interpolator.SetInputData(centerline)
interpolator.Update()
# convert to desired unit
# get the first element pressure
try:
first_point_pressure = interpolator.GetOutput().GetPointData().GetArray("p").GetValue(0)
except:
first_point_pressure = 120
converter = vtk.vtkArrayCalculator()
converter.SetInputData(interpolator.GetOutput())
converter.AddScalarArrayName("p")
converter.SetFunction("120 + (p - {}) * 921 * 0.0075".format(first_point_pressure)) # 921 = mu/nu = density of blood, 0.0075 converts from Pascal to mmHg, offset 120mmHg at ica
converter.SetResultArrayName("p(mmHg)")
converter.Update()
converter2 = vtk.vtkArrayCalculator()
converter2.SetInputData(converter.GetOutput())
converter2.AddVectorArrayName("wallShearStress")
converter2.SetFunction("wallShearStress * 921") # 921 = mu/nu = density of blood, 0.0075 converts from Pascal to mmHg, http://aboutcfd.blogspot.com/2017/05/wallshearstress-in-openfoam.html
converter2.SetResultArrayName("wallShearStress(Pa)")
converter2.Update()
# output the probe centerline
centerline_output_path = os.path.join(
os.path.dirname(centerline_file),
output_dir,
"centerlines",
"centerline_probe_{}.vtp".format(os.path.split(file_name)[1].split("_")[1].split(".")[0])
)
centerlines.append(converter2.GetOutput())
averageFilter.SetInputData(converter2.GetOutput())
averageFilter.Update()
centerline = averageFilter.GetOutput()
# extract lesion section
# get the abscissas of min radius point
# Create kd tree
kDTree = vtk.vtkKdTreePointLocator()
kDTree.SetDataSet(centerline)
kDTree.BuildLocator()
minIdx = kDTree.FindClosestPoint(minPoint)
minPoint_absc = centerline.GetPointData().GetArray("Abscissas_average").GetTuple(minIdx)[0]
thresholdFilter = vtk.vtkThreshold()
thresholdFilter.ThresholdBetween(minPoint_absc-prox_dist,minPoint_absc+dist_dist)
thresholdFilter.SetInputData(centerline)
thresholdFilter.SetAllScalars(0) # important !!!
thresholdFilter.SetInputArrayToProcess(0, 0, 0, vtk.vtkDataObject.FIELD_ASSOCIATION_POINTS,"Abscissas_average");
thresholdFilter.Update()
# to vtkpolydata
geometryFilter = vtk.vtkGeometryFilter()
geometryFilter.SetInputData(thresholdFilter.GetOutput())
geometryFilter.Update()
# get closest line
connectFilter = vtk.vtkConnectivityFilter()
connectFilter.SetExtractionModeToClosestPointRegion()
connectFilter.SetClosestPoint(minPoint)
connectFilter.SetInputData(geometryFilter.GetOutput())
connectFilter.Update()
# compute result values
abscissas = vtk_to_numpy(thresholdFilter.GetOutput().GetPointData().GetArray("Abscissas_average"))
abscissas_unique, index = np.unique(sorted(abscissas), axis=0, return_index=True)
pressure = vtk_to_numpy(thresholdFilter.GetOutput().GetPointData().GetArray("p(mmHg)_average"))
pressure = [x for _, x in sorted(zip(abscissas,pressure))]
pressure = [pressure[x] for x in index]
pressure_gradient = np.diff(pressure)/np.diff(abscissas_unique)
velocity = vtk_to_numpy(thresholdFilter.GetOutput().GetPointData().GetArray("U_average"))
velocity = [math.sqrt(value[0]**2 + value[1]**2 +value[2]**2 ) for value in velocity]
velocity = [x for _, x in sorted(zip(abscissas,velocity))]
velocity = [velocity[x] for x in index]
velocity_gradient = np.diff(velocity)/np.diff(abscissas_unique)
vorticity = vtk_to_numpy(thresholdFilter.GetOutput().GetPointData().GetArray("vorticity_average"))
vorticity = [math.sqrt(value[0]**2 + value[1]**2 +value[2]**2 ) for value in vorticity]
vorticity = [x for _, x in sorted(zip(abscissas,vorticity))]
vorticity = [vorticity[x] for x in index]
vorticity_gradient = np.diff(vorticity)/np.diff(abscissas_unique)
wss = vtk_to_numpy(thresholdFilter.GetOutput().GetPointData().GetArray("wallShearStress(Pa)_average"))
wss = [math.sqrt(value[0]**2 + value[1]**2 +value[2]**2 ) for value in wss]
wss = [wss[x] for x in index]
wss = [x for _, x in sorted(zip(abscissas,wss))]
wss_gradient = np.diff(wss)/np.diff(abscissas_unique)
epsilon = 0.00001
p_ratio = np.mean(pressure[-4:-1])/(np.mean(pressure[0:3])+epsilon)
u_ratio = np.mean(velocity[-4:-1])/(np.mean(velocity[0:3])+epsilon)
w_ratio = np.mean(vorticity[-4:-1])/(np.mean(vorticity[0:3])+epsilon)
wss_ratio = np.mean(wss[-4:-1])/(np.mean(wss[0:3])+epsilon)
dp_ratio = np.mean(pressure_gradient[-4:-1])/(np.mean(pressure_gradient[0:3])+epsilon)
du_ratio = np.mean(velocity_gradient[-4:-1])/(np.mean(velocity_gradient[0:3])+epsilon)
dw_ratio = np.mean(vorticity_gradient[-4:-1])/(np.mean(vorticity_gradient[0:3])+epsilon)
dwss_ratio = np.mean(wss_gradient[-4:-1])/(np.mean(wss_gradient[0:3])+epsilon)
return_value = {
'translesion peak presssure(mmHg)': np.mean(heapq.nlargest(1, pressure)),
'translesion presssure ratio': p_ratio,
'translesion peak pressure gradient(mmHgmm^-1)': np.mean(heapq.nlargest(1, pressure_gradient)),
'translesion pressure gradient ratio': dp_ratio,
'translesion peak velocity(ms^-1)': np.mean(heapq.nlargest(1, velocity)),
'translesion velocity ratio': u_ratio,
'translesion velocity gradient ratio': du_ratio,
'translesion peak velocity gradient(ms^-1mm^-1)': np.mean(heapq.nlargest(1, velocity_gradient)),
'translesion peak vorticity(ms^-1)': np.mean(heapq.nlargest(1, vorticity)),
'translesion vorticity ratio': w_ratio,
'translesion vorticity gradient ratio': dw_ratio,
'translesion peak vorticity gradient(Pamm^-1)':np.mean(heapq.nlargest(1, vorticity_gradient)),
'translesion peak wss(Pa)': np.mean(heapq.nlargest(1, wss)),
'translesion peak wss gradient(Pamm^-1)': np.mean(heapq.nlargest(1, wss_gradient)),
'translesion wss ratio': wss_ratio,
'translesion wss gradient ratio': dwss_ratio,
}
return return_value
def result_analysis(case_dir, minPoint=(0,0,0), maxPoint=(0,0,0), probe=False ,stenosis=True, perform_fit=False):
# load domain json file
with open(os.path.join(case_dir,"domain.json")) as f:
domain = json.load(f)
centerline = os.path.join(case_dir, domain["centerline"]["filename"])
surface = os.path.join(case_dir, domain["domain"]["filename"])
output_dir = os.path.join(case_dir,"CFD_OpenFOAM_result")
return_value = {}
if stenosis:
try:
assert domain["fiducial_0"]["type"] == "Stenosis", "\"fiducial_0\" is not Stenosis"