# `aug_emsc_distort` — E M S C Distortion Augmenter

_Group_: **Augmentation** · _Binding_: `n4m.sklearn.EMSCDistortionAugmenter` · _C ABI_: `n4m_aug_emsc_distort_*`

## Description

Random EMSC-like multiplicative, additive and polynomial distortion.

### Parameters

| Name | Type | Default |
|------|------|---------|
| `mult_low` | `float` | `0.9` |
| `mult_high` | `float` | `1.1` |
| `add_low` | `float` | `-0.05` |
| `add_high` | `float` | `0.05` |
| `polynomial_order` | `int` | `2` |
| `polynomial_strength` | `float` | `0.02` |
| `correlation` | `float` | `0.3` |
| `wavelengths` | `—` | `None` |
| `rng` | `Optional[PCG64]` | `None` |
| `seed` | `int` | `0` |

## Explanations

### Bibliographic source

_Standard spectroscopic operator — see the nirs4all preprocessing / augmentation handbook and the cited literature within the binding docstring._

### Mathematical principle

Random EMSC-like multiplicative, additive and polynomial distortion.

### Implementation

C ABI `n4m_aug_emsc_distort_*` in libn4m (create / apply / destroy lifecycle), wrapped by `n4m.sklearn.EMSCDistortionAugmenter`. The same numerical kernel backs every language binding.

### Usage

```python
from n4m.sklearn import EMSCDistortionAugmenter
op = EMSCDistortionAugmenter()
X_transformed = op.fit_transform(X)
```

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_See also_: [methods index](index.md) · [interactive dashboard](../landing/dashboard.md)