BaseTransformer#

class mobgap.data_transform.base.BaseTransformer[source]#

Base class for all data transformers.

Methods

clone()

Create a new instance of the class with all parameters copied over.

get_params([deep])

Get parameters for this algorithm.

set_params(**params)

Set the parameters of this Algorithm.

transform(data, **kwargs)

Transform the data using the transformer.

__init__(*args, **kwargs)#
clone() Self[source]#

Create a new instance of the class with all parameters copied over.

This will create a new instance of the class itself and all nested objects

get_params(deep: bool = True) dict[str, Any][source]#

Get parameters for this algorithm.

Parameters:
deep

Only relevant if object contains nested algorithm objects. If this is the case and deep is True, the params of these nested objects are included in the output using a prefix like nested_object_name__ (Note the two “_” at the end)

Returns:
params

Parameter names mapped to their values.

set_params(**params: Any) Self[source]#

Set the parameters of this Algorithm.

To set parameters of nested objects use nested_object_name__para_name=.

transform(
data: Series | DataFrame | ndarray,
**kwargs: Unpack[dict[str, Any]],
) Self[source]#

Transform the data using the transformer.

Parameters:
data

A dataframe representing single sensor data.

kwargs

Further keyword arguments for the transformer.

Returns:
self

The instance of the transformer with the results attached

Examples using mobgap.data_transform.base.BaseTransformer#

Generic Data Transforms

Generic Data Transforms

General Filter Introduction

General Filter Introduction

Resampling data

Resampling data

Continuous Wavelet Transform (CWT) - Filter

Continuous Wavelet Transform (CWT) - Filter

Gaussian Smoothing

Gaussian Smoothing

Savitzky-Golay Filter Example

Savitzky-Golay Filter Example