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]