I want to explore more in my VTU file
<?xml version='1.0'?>
<VTKFile type='UnstructuredGrid' version='0.1' byte_order='LittleEndian' compressor='vtkZLibDataCompressor'>
<UnstructuredGrid>
<Piece NumberOfPoints='1368' NumberOfCells='342'>
<PointData>
</PointData>
<CellData Scalars='area failure mode length opening 0 opening 1 opening 2 property id slip 0 slip 1 slip 2 '>
<DataArray Name='area' type='Float64' NumberOfComponents='1' format='appended' offset='0'></DataArray>
<DataArray Name='failure mode' type='Float32' NumberOfComponents='1' format='appended' offset='200'></DataArray>
<DataArray Name='length' type='Float64' NumberOfComponents='1' format='appended' offset='256'></DataArray>
<DataArray Name='opening 0' type='Float64' NumberOfComponents='1' format='appended' offset='448'></DataArray>
<DataArray Name='opening 1' type='Float64' NumberOfComponents='1' format='appended' offset='636'></DataArray>
<DataArray Name='opening 2' type='Float64' NumberOfComponents='1' format='appended' offset='824'></DataArray>
<DataArray Name='property id' type='Int32' NumberOfComponents='1' format='appended' offset='1020'></DataArray>
<DataArray Name='slip 0' type='Float64' NumberOfComponents='1' format='appended' offset='1076'></DataArray>
<DataArray Name='slip 1' type='Float64' NumberOfComponents='1' format='appended' offset='1272'></DataArray>
<DataArray Name='slip 2' type='Float64' NumberOfComponents='1' format='appended' offset='1468'></DataArray>
</CellData>
<Points>
<DataArray type='Float64' NumberOfComponents='3' format='appended' offset='1660'></DataArray>
</Points>
<Cells>
<DataArray Name='connectivity' type='Int32' NumberOfComponents='1' format='appended' offset='10776'></DataArray>
<DataArray Name='offsets' type='Int32' NumberOfComponents='1' format='appended' offset='13384'></DataArray>
<DataArray Name='types' type='UInt8' NumberOfComponents='1' format='appended' offset='14032'></DataArray>
</Cells>
</Piece>
</UnstructuredGrid>
<AppendedData encoding='base64'>
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AQAAAFgFAABYBQAA1AEAAA==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eNrj4BgF1AcAKqcKsQ==
</AppendedData>
</VTKFile>
I try to write a Python script to read my VTU file, like store all the results to lists, so I can do some analysis.
I have tried both two packages from this link,
Reading data from a raw VTK (.vtu) file
but they don't work.
import meshio
mesh = meshio.read("readtestfile.vtu")
import vtk.vtk
# The source file
file_name = "/readtestfile.vtu"
# Read the source file.
reader = vtk.vtkXMLUnstructuredGridReader()
reader.SetFileName(file_name)
reader.Update() # Needed because of GetScalarRange
output = reader.GetOutput()
potential = output.GetPointData().GetArray("potential")