elastic_noiser

Defines the ElasticNoiser.

class ElasticNoiser(k: Distribution[float], invert: bool = False, exponential: bool = False)

Bases: Noiser

The ElasticNoiser applies noise elastically.

Noise is added randomly at every point using a normal distributions where the variance increases with t.

k

maximal variance of the normal distributions

invert

If True, noise is added at the beggining of the curve.

exponential

If True, uses exponentially increasing noise. If invert = True, exponentially decreasing.

Inits ElasticNoiser with the provided params.

Parameters:

• k – maximal variance of the normal distributions

• invert – If True, noise is added at the beggining of the curve.

• exponential – If True, uses exponentially increasing noise. If invert = True, exponentially decreasing.

noisify(signal: DataFrame) → DataFrame

Applies noise to the provided signal.

Applies random gaussian noise with increasing or decreasing variance over time to the signal.

Parameters:

signal – The input data.

Returns:

pd.DataFrame

Example

>>> import pandas as pd
>>> from simba_ml.simulation import distributions
>>> from simba_ml.simulation.noisers.elastic_noiser import ElasticNoiser
>>> series = [[0]] * 1000
>>> df = pd.DataFrame(series)
>>> df.head()
   0
0  0
1  0
2  0
3  0
4  0
>>> noisers = ElasticNoiser(distributions.Constant(10))
>>> noisers.noisify(df).plot()