# Do some math on VTU file

**URL:** https://discourse.paraview.org/t/do-some-math-on-vtu-file/8925
**Category:** ParaView Support
**Created:** [February 7, 2022, 8:53pm UTC](https://discourse.paraview.org/t/do-some-math-on-vtu-file/8925 "2022-02-07T20:53:46Z")
**Posts on this page:** 6
**Page:** 1

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### Author: ![Miraboreasau](https://discourse.paraview.org/letter_avatar_proxy/v4/letter/m/cc9497/32.png) [@Miraboreasau](https://discourse.paraview.org/u/Miraboreasau)
#### Post date: [February 7, 2022, 8:53pm UTC](https://discourse.paraview.org/t/do-some-math-on-vtu-file/8925/1 "2022-02-07T20:53:46Z")

</div>

I have a VTU file like this, (too big to upload, so I copy part of it). What I used to do is use MATLAB/Python to read the value of certain DataArray, but this time, the file is encoded, what is the best way to decode it, and then use MATLAB or Python script, or how to do some math directly in Paraview, like is there a good tutorial?

```auto
<VTKFile type='UnstructuredGrid' version='0.1' byte_order='LittleEndian' compressor='vtkZLibDataCompressor'>
	<UnstructuredGrid>
		<Piece NumberOfPoints='233202' NumberOfCells='77734'>
				<PointData Vectors='displacement force velocity ' Scalars='boundary condition ID fluid pressure '>
		<DataArray Name='displacement' type='Float64' NumberOfComponents='3' format='appended' offset='0'></DataArray>
		<DataArray Name='force' type='Float64' NumberOfComponents='3' format='appended' offset='3985032'></DataArray>
		<DataArray Name='velocity' type='Float64' NumberOfComponents='3' format='appended' offset='8977484'></DataArray>
		<DataArray Name='boundary condition ID' type='Int32' NumberOfComponents='1' format='appended' offset='13996524'></DataArray>
		<DataArray Name='fluid pressure' type='Float64' NumberOfComponents='1' format='appended' offset='13998032'></DataArray>
	</PointData>
				<CellData Tensors='principal deviatoric stresses principal strains principal stresses strains stresses ' Scalars='alive mass material property ID mean stress volumetric strain '>
		<DataArray Name='principal deviatoric stresses' type='Float64' NumberOfComponents='9' format='appended' offset='14000496'></DataArray>
		<DataArray Name='principal strains' type='Float64' NumberOfComponents='9' format='appended' offset='15262288'></DataArray>
		<DataArray Name='principal stresses' type='Float64' NumberOfComponents='9' format='appended' offset='17071920'></DataArray>
		<DataArray Name='strains' type='Float64' NumberOfComponents='9' format='appended' offset='18896468'></DataArray>
		<DataArray Name='stresses' type='Float64' NumberOfComponents='9' format='appended' offset='21325604'></DataArray>
		<DataArray Name='alive' type='Int32' NumberOfComponents='1' format='appended' offset='24122624'></DataArray>
		<DataArray Name='mass' type='Float64' NumberOfComponents='1' format='appended' offset='24123084'></DataArray>
		<DataArray Name='material property ID' type='Int32' NumberOfComponents='1' format='appended' offset='24903008'></DataArray>
		<DataArray Name='mean stress' type='Float64' NumberOfComponents='1' format='appended' offset='24903464'></DataArray>
		<DataArray Name='volumetric strain' type='Float64' NumberOfComponents='1' format='appended' offset='25703828'></DataArray>
	</CellData>
				<Points>
		<DataArray type='Float64' NumberOfComponents='3' format='appended' offset='26502476'></DataArray>
	</Points>
				<Cells>
		<DataArray Name='connectivity' type='Int32' NumberOfComponents='1' format='appended' offset='31321668'></DataArray>
		<DataArray Name='offsets' type='Int32' NumberOfComponents='1' format='appended' offset='31751792'></DataArray>
		<DataArray Name='types' type='UInt8' NumberOfComponents='1' format='appended' offset='31896096'></DataArray>
	</Cells>
		</Piece>
	</UnstructuredGrid>
<AppendedData encoding='base64'>
_AQAAALBmVQCwZlUA05otAA==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

```

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<div class="post-metadata">

### Author: ![Christos\_Tsolakis](https://discourse.paraview.org/user_avatar/discourse.paraview.org/christos_tsolakis/32/4450_2.png) [@Christos\_Tsolakis](https://discourse.paraview.org/u/Christos_Tsolakis)
#### Post date: [February 7, 2022, 9:20pm UTC](https://discourse.paraview.org/t/do-some-math-on-vtu-file/8925/2 "2022-02-07T21:20:09Z")

</div>

If you just want to decode the data, open the file with ParaView and then save it again by setting the `Data Mode` to `Ascii` instead of `Appended`. See below

 ![Screenshot from 2022-02-07 16-13-03](https://discourse.paraview.org/uploads/default/original/2X/e/e6e8e9d8fbeefe639e7bb6375fa274711110d26b.png)

To perform calculations on the data you may use the [Calculator Filter](https://docs.paraview.org/en/latest/UsersGuide/filteringData.html#calculator) or even the [Python Calculator](https://docs.paraview.org/en/latest/UsersGuide/filteringData.html#python-calculator).

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<div class="post-metadata">

### Author: ![Miraboreasau](https://discourse.paraview.org/letter_avatar_proxy/v4/letter/m/cc9497/32.png) [@Miraboreasau](https://discourse.paraview.org/u/Miraboreasau)
#### Post date: [February 8, 2022, 4:58am UTC](https://discourse.paraview.org/t/do-some-math-on-vtu-file/8925/3 "2022-02-08T04:58:54Z")

</div>

Thanks, but I found another issue, I calculated one result, like result1, then I calculated another one (result2), the new one will cover my result1, making the previous one become result1(?), then it disappears. I don’t want to create many calculators in the list, I want them all inside one scalar.  
 ![image](https://discourse.paraview.org/uploads/default/original/2X/f/fc39ad36bcd59676ec5a8ea5d136512542e0a054.png)

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<div class="post-metadata">

### Author: ![mwestphal](https://discourse.paraview.org/user_avatar/discourse.paraview.org/mwestphal/32/17_2.png) [@mwestphal](https://discourse.paraview.org/u/mwestphal)
#### Post date: [February 8, 2022, 9:06am UTC](https://discourse.paraview.org/t/do-some-math-on-vtu-file/8925/4 "2022-02-08T09:06:38Z")

</div>

Use a more recent version of ParaView, like 5.10

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<div class="post-metadata">

### Author: ![Miraboreasau](https://discourse.paraview.org/letter_avatar_proxy/v4/letter/m/cc9497/32.png) [@Miraboreasau](https://discourse.paraview.org/u/Miraboreasau)
#### Post date: [February 8, 2022, 4:26pm UTC](https://discourse.paraview.org/t/do-some-math-on-vtu-file/8925/5 "2022-02-08T16:26:18Z")

</div>

Hello, I just download the ParaView a week ago, and it is 5.10.

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### Author: ![Christos\_Tsolakis](https://discourse.paraview.org/user_avatar/discourse.paraview.org/christos_tsolakis/32/4450_2.png) [@Christos\_Tsolakis](https://discourse.paraview.org/u/Christos_Tsolakis)
#### Post date: [February 8, 2022, 4:50pm UTC](https://discourse.paraview.org/t/do-some-math-on-vtu-file/8925/6 "2022-02-08T16:50:27Z")

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> Thanks, but I found another issue, I calculated one result, like result1, then I calculated another one (result2), the new one will cover my result1, making the previous one become result1(?), then it disappears. I don’t want to create many calculators in the list, I want them all inside one scalar.

The problem here is that you are overwriting the previous calculation. Each `Calculator` instance produces a new `result<x>`. To (re)use `result0` you need a new `Calculator` instance. To avoid having multiple `result<x>` combine the calculations into one formula/expression.