> ## Documentation Index
> Fetch the complete documentation index at: https://docs.edgeimpulse.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Flatten
**The Flatten** block performs statistical analysis on the signal. It is useful for slow-moving averages like temperature data, in combination with other blocks.
GitHub repository containing all DSP block code: [edgeimpulse/processing-blocks](https://github.com/edgeimpulse/processing-blocks).
## Flatten parameters
**Normalize features**
To enable data normalization, you can use the **Normalize features** option in the processing blocks **generate features** tab. This will learn the mean and standard deviation of each output column during the feature generation step, and apply a normalization step during training and inference.
When enabled, the block learns the mean and standard deviation of every output column during **Generate features**, then applies a normalization step at training and inference. Recommended when raw numeric ranges differ by more than \~10×. see [Why normalize?](/studio/projects/processing-blocks#why-normalize) for more details.
**Scaling**
* Scale axes: Multiplies axes by this number
**Method**
* Average: Calculates the average value for the window
* Minimum: Calculates the minimum value in the window
* Maximum: Calculates the maximum value in the window
* Root-mean square: Calculates the RMS value of the window
* Standard deviation: Calculates the standard deviation of the window
* Skewness: Calculates the skewness of the window
* Kurtosis: Calculates the kurtosis of the window
* Moving Average Number of Windows: Calculates the moving average by maintaining a rolling average of the last N windows. Note, there is no zero padding, the block will accumulate averages up to N windows. (Ex. for the first window in a sample, the moving average will equal the average). The moving average resets for each sample during training, and during inference, when run\_classifier\_init() is called. Note if you enable this, you probably don't want overlapping windows for training.
## How does the flatten block work?
The **Flatten** block first rescales axes of the signal if value is different than 1. Then statistical analysis is performed on each window, computing between 1 and 8 features for each axis, depending on the number of selected methods.