gaitmap.data_transform.BaseSlidingWindowFeatureTransform#

class gaitmap.data_transform.BaseSlidingWindowFeatureTransform(window_size_s: float | None = None)[source]#

Baseclass for all Sliding window feature transforms.

Attributes:
effective_window_size_samples_

Get the real sample size of the window in samples after rounding effects.

Methods

clone()

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

from_json(json_str)

Import an gaitmap object from its json representation.

get_params([deep])

Get parameters for this algorithm.

set_params(**params)

Set the parameters of this Algorithm.

to_json()

Export the current object parameters as json.

transform(data, *[, sampling_rate_hz])

Apply the transformation on each sliding window.
__init__(window_size_s: float | None = None) → None[source]#
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

classmethod from_json(json_str: str) → Self[source]#

Import an gaitmap object from its json representation.

For details have a look at the this example.

You can use the to_json method of a class to export it as a compatible json string.

Parameters:
json_str

json formatted string

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=.

to_json() → str[source]#

Export the current object parameters as json.

For details have a look at the this example.

You can use the from_json method of any gaitmap algorithm to load the object again.

Warning

This will only export the Parameters of the instance, but not any results!

transform(data: DataFrame, *, sampling_rate_hz: float | None = None, **kwargs) → Self[source]#

Apply the transformation on each sliding window.

Parameters:
data

data to be filtered

sampling_rate_hz

The sampling rate of the data in Hz