# Is there a filter to normalise the input data?

**URL:** https://discourse.paraview.org/t/is-there-a-filter-to-normalise-the-input-data/10389
**Category:** ParaView Support
**Created:** [September 22, 2022, 4:04pm UTC](https://discourse.paraview.org/t/is-there-a-filter-to-normalise-the-input-data/10389 "2022-09-22T16:04:13Z")
**Posts on this page:** 3
**Page:** 1

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### Author: ![hxhxhx33](https://discourse.paraview.org/user_avatar/discourse.paraview.org/hxhxhx33/32/10030_2.png) [@hxhxhx33](https://discourse.paraview.org/u/hxhxhx33)
#### Post date: [September 22, 2022, 4:04pm UTC](https://discourse.paraview.org/t/is-there-a-filter-to-normalise-the-input-data/10389/1 "2022-09-22T16:04:13Z")

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I am rendering a 3D medical image, and want to normalize the input data to have a zero mean and unit variance, i.e. to subtract from each voxel the mean and then divide it by the std. I have googled but failed to find a solution. May I ask if there is a existing filter, or how I can achieve this? Thanks.

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### Author: ![Kenichiro-Yoshimi](https://discourse.paraview.org/letter_avatar_proxy/v4/letter/k/ecc23a/32.png) [@Kenichiro-Yoshimi](https://discourse.paraview.org/u/Kenichiro-Yoshimi)
#### Post date: [September 27, 2022, 10:12am UTC](https://discourse.paraview.org/t/is-there-a-filter-to-normalise-the-input-data/10389/2 "2022-09-27T10:12:09Z")

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Hi,

The `Python Calculator` filter may be used to standardize values for image data.

The following is an example of applying it to a `Wavelet` source. Here, the following is written in `Expression`:

```python
(RTData-mean(RTData))/std(RTData)

```

 ![standardization](https://discourse.paraview.org/uploads/default/original/2X/1/1dedf9aae4f8c8bec0bb5c3b1a99a76eaae71c78.png)

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### Author: ![hxhxhx33](https://discourse.paraview.org/user_avatar/discourse.paraview.org/hxhxhx33/32/10030_2.png) [@hxhxhx33](https://discourse.paraview.org/u/hxhxhx33)
#### Post date: [September 27, 2022, 10:31am UTC](https://discourse.paraview.org/t/is-there-a-filter-to-normalise-the-input-data/10389/3 "2022-09-27T10:31:51Z")

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Thanks!