normalization

See: Co-Pilot Halfwidth Cube Plot Scaling Algorithm

Normalization

Normalization is a process used to adjust values measured on different scales to a common scale. It doesn't necessarily involve logarithmic transformation. In general, normalization can mean:

Min-Max Normalization:
- This scales the data to a fixed range, typically [0, 1]. It's done by subtracting the minimum value and dividing by the range (maximum - minimum).
python
```
normalized_value = (value - min_value) / (max_value - min_value)
```
Z-score Normalization (Standardization):
- This centers the data around the mean (0) and scales it by the standard deviation. This transformation is useful when the data has a Gaussian (bell-shaped) distribution.
python
```
normalized_value = (value - mean) / standard_deviation
```
Decimal Scaling:
- This scales the data by moving the decimal point of values, which is determined by the maximum absolute value in the dataset.
python
```
normalized_value = value / 10^j
```
where jj is the smallest integer such that max(|normalized_value|) < 1.
Logarithmic Normalization:
- This applies a log transformation to compress the range of the data. This is particularly useful for data with large variations.
python
```
normalized_value = log(value)
```