niimpy.preprocessing.tracker module

niimpy.preprocessing.tracker.extract_features_tracker(df, features=None)

This function computes and organizes the selected features for tracker data recorded using Polar Ignite.

The complete list of features that can be calculated are: tracker_daily_step_distribution

Parameters

df: pandas.DataFrame

Input data frame

features: dict, optional

Dictionary keys contain the names of the features to compute. The value of the keys is the list of parameters that will be passed to the function. If none is given, all features will be computed.

Returns

result: dataframe

Resulting dataframe

niimpy.preprocessing.tracker.group_data(df, columns=['user', 'device'])

Group the dataframe by a standard set of columns listed in group_by_columns.

niimpy.preprocessing.tracker.reset_groups(df, columns=['user', 'device'])

Group the dataframe by a standard set of columns listed in group_by_columns.

niimpy.preprocessing.tracker.step_summary(df, config={})

Return the summary of step count in a time range. The summary includes the following information of step count per day: mean, standard deviation, min, max

Parameters

dfPandas Dataframe

Dataframe containing the hourly step count of an individual. The dataframe must be date time index.

config: dict

Dictionary keys containing optional arguments. These can be:

value_col: str.

Column contains step values. Default value is “values”.

user_id: list. Optional

List of user id. If none given, returns summary for all users.

start_date: string. Optional

Start date of time segment used for computing the summary. If not given, acquire summary for the whole time range.

end_date: string. Optional

End date of time segment used for computing the summary. If not given, acquire summary for the whole time range.

Returns

summary_df: pandas DataFrame

A dataframe containing user id and associated step summary.

niimpy.preprocessing.tracker.tracker_daily_step_distribution(steps_df, config={})

Return distribution of steps within each day. Assuming the step count is recorded at hourly resolution, this function will compute the contribution of each hourly step count into the daily count (percentage wise).

Parameters

steps_dfPandas Dataframe

Dataframe containing the hourly step count of an individual.

Returns

df: pandas DataFrame

A dataframe containing the distribution of step count per day at hourly resolution.