niimpy.exploration.missingness module

niimpy.exploration.missingness.missing_data_format(question, keep_values=False)

Returns a series of timestamps in the right format to allow missing data visualization .

Parameters:

question: Dataframe

niimpy.exploration.missingness.missing_noise(database, subject, start=None, end=None)

Returns a Dataframe with the estimated missing data from the ambient noise sensor.

NOTE: This function aggregates data by day.

Parameters:

database: Niimpy database

user: string

start: datetime, optional

end: datetime, optional

Returns:

avg_noise: Dataframe

niimpy.exploration.missingness.screen_missing_data(database, subject, start=None, end=None)

Returns a DataFrame contanining the percentage (range [0,1]) of loss data calculated based on the transitions of screen status. In general, if screen_status(t) == screen_status(t+1), we declared we have at least one missing point.

Parameters:

database: Niimpy database

user: string

start: datetime, optional

end: datetime, optional

Returns:

count: Dataframe