Python脚本，用于绘制诸如Paraview这样的图表的演变图

Question

我想编写一个Python脚本，以生成类似于Paraview的下一个屏幕快照中右侧所示的图：

我有使用命令foamToVTK生成的一系列文件：

VTK中是否有类似于Paraview的PlotOverLine方法的功能？

Answer 1

ParaView PlotOverLine在vtk中作为vtkProbeFilter。

最小工作示例：

import vtk

# Original data
source = vtk.vtkRTAnalyticSource()

# the line to plot over
line = vtk.vtkLineSource()

# filter
probeFilter = vtk.vtkProbeFilter()
probeFilter.SetInputConnection(line.GetOutputPort())
probeFilter.SetSourceConnection(source.GetOutputPort())

# rendering
plot = vtk.vtkXYPlotActor()
plot.AddDataSetInputConnection(probeFilter.GetOutputPort())
plot.SetTitle("My plot")
window = vtk.vtkRenderWindow()
interactor = vtk.vtkRenderWindowInteractor()
interactor.SetRenderWindow(window)
renderer = vtk.vtkRenderer()
renderer.AddActor2D(plot)
window.AddRenderer(renderer)
window.Render()
interactor.Start()

您可以在此处找到更复杂的（多图，颜色...）c ++示例：https://lorensen.github.io/VTKExamples/site/Cxx/Annotation/XYPlot/python API相同。

Answer 2

我想出了解决这个问题的方法。不过，这可能不是最佳选择。对于此解决方案，我首先将网格转换为vtkUnstructuredGrid（在这种情况下，使用256点的分辨率。）

下面我附上我用来执行此操作的代码：

import matplotlib.pyplot as plt
from scipy.interpolate import griddata
import numpy as np
import vtk
from vtk.util.numpy_support import vtk_to_numpy
from os import walk, path, system
import pandas as pd

### Create the VTK files
system("foamToVTK")

### Initialization of variables
cnt=1
fig= plt.figure()
npts = 256  #dimensions of the grid

### Get the file names of each step of the simulation
(dirpath, dirnames, filenames) = next(walk('VTK'))
ids=[]
for dir in dirnames:
    ids.append(int(dir.split("_")[1]))
ids = sorted(ids)
basename = dirnames[0].split("_")[0]

### Iteration of time steps
for id in ids[1:]:

    ### Read values from the file of this time step
    filename = "%s/%s_%d/internal.vtu" % (dirpath, basename, id)
    reader = vtk.vtkXMLUnstructuredGridReader()
    reader.SetFileName(filename)
    reader.Update()

    ### Get the coordinates of nodes in the mesh using VTK methods
    nodes_vtk_array= reader.GetOutput().GetPoints().GetData()
    vtk_array = reader.GetOutput().GetPointData().GetArray('U') #Velocity (3 dimensions)
    numpy_array = vtk_to_numpy(vtk_array)
    nodes_nummpy_array = vtk_to_numpy(nodes_vtk_array)
    x,y,z= nodes_nummpy_array[:,0] , nodes_nummpy_array[:,1] , nodes_nummpy_array[:,2]
    xmin, xmax = min(x), max(x)
    ymin, ymax = min(y), max(y)

    ### Define grid
    xi = np.linspace(xmin, xmax, npts)
    yi = np.linspace(ymin, ymax, npts)

    ### Grid the data
    interpolated = griddata((x, y), numpy_array, (xi[None,:], yi[:,None]), method='cubic')

    ### Create the list of points
    plotOverLine=[]
    for point in range(len(interpolated[0])):
        plotOverLine.append(interpolated[127][point])

    ### Update and plot the chart for this time step
    df = pd.DataFrame(plotOverLine, columns=['X', 'Y', 'Z'])
    plt.clf()
    plt.title('Frame %d' % cnt)
    plt.plot(df)
    plt.legend(df.columns)
    axes = plt.gca()
    axes.set_ylim([-15,10])
    plt.draw()
    plt.pause(.05)

对于每个时间步，它都会更新并显示如下图：