4Dflow_python_codes_kh/IVP_visualization.py at main · kw1218/4Dflow_python_codes_kh · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
import sys
import os
import os.path as osp
import numpy as np
from glob import glob
import pyvista as pv
import descriptors_utils as dut
from matplotlib import pyplot as plt
import pandas as pd
from vtkmodules.vtkRenderingAnnotation import vtkAxesActor


# -----------------------------------------------------------------------------------------------------------------------
# rootDir=r'D:\InletProfileStudy\SSM\Output_2024\Circular\synthetic_cohort_first8modes'  # path to synthetic files or sampled profiles
Out_Dir = r'D:/InletProfileStudy/SSM/Input/flow_csv'
# IVP_number = '010'
# IVP_path = osp.join(rootDir,IVP_number)
# vtp_path = osp.join(IVP_path, '*.vtp')

Output_csv = False
# os.makedirs(Out_Dir, exist_ok=True)


rootDir=r'D:\InletProfileStudy\ReadandScale\scaling\BH_2025'
vtp_path = osp.join(rootDir, '*.vtp')

# -----------------------------------------------------------------------------------------------------------------------
## Read synthetic profiles

input_vtps = pv.read(sorted(glob((vtp_path))))


## Flow rate
# Compute flow rate and convert to l/min
#flow_rate = dut.compute_flowrate(input_vtps)['Q(t)'] *60000  # l/min: for patient-specific profiles visualization
flow_rate = dut.compute_flowrate(input_vtps)['Q(t)'] * 20  # for synthetic profiles visualization
max_flow_rate = np.max(flow_rate)
frame_max_flow_rate = np.argmax(flow_rate)

# Plot the flow rate
plt.figure(figsize=(10, 6))
plt.plot(flow_rate, label='Flow Rate (l/min)', color='b', marker='o')


# Write out flow rate to a csv file
if Output_csv:
    flow_rate_ls =  flow_rate / 60000  # convert to l/s
    df_flow = pd.DataFrame(flow_rate_ls)
    df_flow.to_csv(osp.join(Out_Dir, 'flow_rate.csv'), index=False)


# Add labels and title
plt.xlabel('Time (frames)')
plt.ylabel('Flow Rate (l/min)')
plt.title('Flow Rate Over Time')
plt.legend()

# # Show the plot
plt.show()
#plt.savefig(osp.join(Fig_Dir, 'flow_rate.png'))


pl = pv.Plotter()
pl.add_mesh(input_vtps[int(frame_max_flow_rate)], clim=[0,1], scalars='Velocity', cmap='jet')
#pl.add_mesh(interp_planes[3], clim=[0,1.4], scalars='Velocity', cmap='jet')
pl.camera_position = 'xy'
pl.camera.zoom(1.3)
pl.remove_scalar_bar()
pl.set_background('white')
pl.show_axes()
pl.show()

plotter = pv.Plotter()
plotter.add_mesh(input_vtps[int(frame_max_flow_rate)].warp_by_vector(factor=0.1), scalars='Velocity',clim=[0, 0.7], cmap='jet')
scalar_bar = plotter.add_scalar_bar(title='Velocity', n_labels=2,shadow=True, italic=True)


# Access the scalar bar actor
scalar_bar_actor = plotter.scalar_bar

# Change the scalar bar title text color to black
scalar_bar_actor.GetTitleTextProperty().SetColor(0, 0, 0)  # RGB for black

# Change the scalar bar label text color to black
scalar_bar_actor.GetLabelTextProperty().SetColor(0, 0, 0)  # RGB for black

# Set the camera to view along the xy-plane
plotter.camera_position = 'xy'

# Set the background color
plotter.set_background('white')

# # Add axes actor
#axes = vtkAxesActor()
# # Customize the color of axes labels
# axes.GetXAxisCaptionActor2D().GetTextActor().GetTextProperty().SetColor(0, 0, 0)  # X-axis label color
# axes.GetYAxisCaptionActor2D().GetTextActor().GetTextProperty().SetColor(0, 0, 0)  # Y-axis label color
# axes.GetZAxisCaptionActor2D().GetTextActor().GetTextProperty().SetColor(0, 0, 0)  # Z-axi

# # Change the font size of the axes labels
# axes.GetXAxisCaptionActor2D().GetTextActor().GetTextProperty().SetFontSize(100)  # X-axis font size
# axes.GetYAxisCaptionActor2D().GetTextActor().GetTextProperty().SetFontSize(100)  # Y-axis font size
# axes.GetZAxisCaptionActor2D().GetTextActor().GetTextProperty().SetFontSize(100)  # Z-axis font size

# # Scale the axes size
# axes.SetTotalLength(0.02, 0.02, 0.02)  # Adjust as needed

# # Values between 0 and 1. Adjust to fit desired position.
# axes.SetPosition(0.5,0,0)


#plotter.add_actor(axes)
plotter.camera_position = 'xy'
plotter.set_background('white')
plotter.show_axes()  # Show the axes
plotter.show()