pyActigraphy.metrics.MetricsMixin.M10p

MetricsMixin.M10p(period='7D', binarize=True, threshold=4, verbose=False)[source]

M10 per period

The M10 variable is calculated for each consecutive period found in the actigraphy recording.

Parameters:

  • period (str, optional) – Time period for the calculation of IS Default is ‘7D’.

  • binarize (bool, optional) – If set to True, the data are binarized. Default is True.

  • threshold (int, optional) – If binarize is set to True, data above this threshold are set to 1 and to 0 otherwise. Default is 4.

  • verbose (bool, optional) – If set to True, display the number of periods found in the activity recording, as well as the time not accounted for. Default is False.

Returns:

m10p

Return type:

list of float

Notes

The M10 [1] variable is calculated as the mean, per acquisition period , of the average daily activities during the 10 most active hours.

Warning

The value of this variable depends on the length of the acquisition period.

References

[1]

Van Someren, E.J.W., Lijzenga, C., Mirmiran, M., Swaab, D.F. (1997). Long-Term Fitness Training Improves the Circadian Rest-Activity Rhythm in Healthy Elderly Males. Journal of Biological Rhythms, 12(2), 146–156. http://doi.org/10.1177/074873049701200206

Examples

>>> import pyActigraphy
>>> rawAWD = pyActigraphy.io.read_raw_awd(fpath + 'SUBJECT_01.AWD')
>>> rawAWD.duration()
Timedelta('12 days 18:41:00')
>>> rawAWD.M10p(period='5D',verbose=True)
Number of periods: 2
Time unaccounted for: 2 days, 19h, 0m, 0s
[0.XXXX, 0.XXXX]