gaitmap.data_transform.SlidingWindowMean#
- class gaitmap.data_transform.SlidingWindowMean(window_size_s: float | None = None)[source]#
Calculate a sliding window mean.
- Parameters:
- window_size_s
The window size in seconds
- Other Parameters:
- data
The data passed to the transform method.
- sampling_rate_hz
The sampling rate of the input data
- Attributes:
- transformed_data_
The transformed data.
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.
- 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_jsonmethod 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=.