如何解压缩/解码由python中的vtkZlibDataCompressor压缩的base64附加数据?

时间:2016-03-11 23:09:10

标签: python base64 zlib vtk

我正在尝试解压缩包含以下消息的.vtu文件:

<?xml version="1.0"?>
<VTKFile type="UnstructuredGrid" version="0.1" byte_order="LittleEndian"            compressor="vtkZLibDataCompressor">
      <UnstructuredGrid>
        <Piece NumberOfPoints="680"                  NumberOfCells="334"                 >
          <PointData Vectors="ContactForces" Tensors="STRESS">
            <DataArray type="Float64" Name="VelocityVectors" NumberOfComponents="3" format="appended" offset="0"                   />
            <DataArray type="Float64" Name="ContactForces" NumberOfComponents="3" format="appended" offset="164"                 />
            <DataArray type="Float64" Name="STRESS" NumberOfComponents="9" format="appended" offset="268"                 />
          </PointData>
          <CellData>
          </CellData>
          <Points>
            <DataArray type="Float32" Name="Array 02C5DA30" NumberOfComponents="3" format="appended" offset="488"                 />
          </Points>
          <Cells>
            <DataArray type="Int32" Name="connectivity" format="appended" offset="10596"               />
            <DataArray type="Int32" Name="offsets" format="appended" offset="16896"               />
            <DataArray type="UInt8" Name="types" format="appended" offset="17608"               />
          </Cells>
        </Piece>
      </UnstructuredGrid>
      <AppendedData encoding="base64">
       _AQAAAACAAADAPwAAZwAAAA==eNrtyKENgDAQAMBXLMAcSDwSZugWjILFIEk3qCFdC1UUO1ScuuTyfr6trUvEleajPL/Ze++9995777333nvvvffee++99938cE9jxFa9995777333nvvvffee++999573+9/9W2oGw==AQAAAACAAADAPwAAPAAAAA==eNrtxTEBACAIADCyGIIstCKSFcjDZQfv7VlMnc2+Ydu2bdu2bdu2bdu2bdu2bdu2bdu2bdu2v3/cHyODAgAAAACAAABAPwAAUwAAADwAAAA=eNrtxTEBACAMAzCvPVExZ1MxJTycE0LypDLn5nXZtm3btm3btm3btm3btm3btm3btm3btm3btm3btm3btm3btm3btm3btm3btm3btv3xC87P1M942u3FMREAMAgEMK8/VgXOUIGSLh3R0btkSWXOzeuybdu2bdu2bdu2bdu2bdu2bdu2bdu2bfvjF/TX7iE=AQAAAACAAADgHwAAix0AAA==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AQAAAACAAAA4BQAAAwIAAA==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AQAAAACAAABOAQAADQAAAA==eNqTkBgF1AMAf1IfUQ==
      </AppendedData>
    </VTKFile>

我尝试过以下代码:

import zlib
import base64
original_data = base64.b64encode(data).decode()
zlib.decompress(base64.b64decode(original_data))

但是python回答为:

Error -3 while decompressing data: incorrect header check 

有谁知道如何解压缩附加的数据?因为我真的很困惑....

0 个答案:

没有答案