Statistics

Functions for statistical analysis of biomechanical time series data.

biomechzoo.statistics.eventval.eventval(fld, dim1=None, dim2=None, ch=None, localevts=None, globalevts=None, anthroevts=None)

Extract event values from .zoo files and compile into a pandas DataFrame.

Parameters:

• fld (str) – Path to the root data folder containing .zoo files.

• dim1 (list of str, optional) – List of conditions (subfolder names under fld).

• dim2 (list of str, optional) – List of participant identifiers.

• ch (list of str) – List of channels to extract events from.

• localevts (list of str, optional) – List of local events.

• globalevts (list of str, optional) – List of global events.

• anthroevts (list of str, optional) – List of events stored in the metadata.Usually anthropometric data

Returns:

pd.DataFrame – Columns: [‘condition’, ‘subject’, ‘file’, ‘event_name’, ‘event_value’]

biomechzoo.statistics.lineval.lineval(root_folder, channel_name, output_format='array', subject_level=0, condition_level=1)

Extract time-normalized line arrays from Zoo files.

This function recursively searches root_folder for .zoo files and extracts the line field from the specified channel. Folder levels are used to assign subject and condition labels.

Data must already be time-normalized. The function will raise an error if inconsistent signal lengths are detected.

Parameters:

• root_folder (str) – Root directory containing data.

• channel_name (str) – Name of the channel to extract.

• output_format (Literal['array', 'wide']) – Output format. - 'array': one column containing the full array (default) - 'wide': one column per timepoint (p0, p1, …)

• subject_level (int) – Folder index used to define subject label (0 = first folder below root).

• condition_level (int) – Folder index used to define condition label (0 = first folder below root).

Raises:

• KeyError – If the specified channel or line field is missing.

• ValueError – If signals are not equal length (not normalized).

• ValueError – If invalid format is provided.

• IndexError – If folder depth is insufficient for specified levels.

Returns:

DataFrame containing extracted line data with subject, condition, and trial references.

Return type:

pandas.DataFrame

biomechzoo.statistics.lineval_wide2arrays.lineval_wide2arrays(df, condition_col='condition', conditions=None)

Convert a wide-format DataFrame into arrays grouped by condition.

Parameters:

• df (pd.DataFrame) – DataFrame returned by lineval(format=’wide’). Must include columns for condition and timepoints (p0…pN).

• condition_col (str) – Column name containing condition labels. Default ‘condition’.

• conditions (list of str, optional) – List of condition labels in desired order. If None, will use all unique conditions sorted alphabetically.

Returns:

Dict[str, np.ndarray] – Keys = condition labels Values = 2D numpy arrays (n_trials x n_timepoints)

Parameters:

• df (DataFrame)

• condition_col (str)

• conditions (List[str] | None)

Return type:

Dict[str, ndarray]

biomechzoo.statistics.rmse.rmse(a, b)