Usage

Prerequisite

Python 3.8 or greater

$ python --version  # or python3 --version

Java 8 (1.8.0) or greater
$ java -version

Installation

$ pip install actipy

Basic example

Process an Axivity file (.cwa):

import actipy

data, info = actipy.read_device("sample.cwa.gz",
                                lowpass_hz=20,
                                calibrate_gravity=True,
                                detect_nonwear=True,
                                resample_hz=50)

Output:

data [pandas.DataFrame]
                                x         y         z  temperature
time
2014-05-07 13:29:50.430 -0.513936  0.070043  1.671264    20.000000
2014-05-07 13:29:50.440 -0.233910 -0.586894  0.081946    20.000000
2014-05-07 13:29:50.450 -0.080303 -0.951132 -0.810433    20.000000
2014-05-07 13:29:50.460 -0.067221 -0.976200 -0.864934    20.000000
2014-05-07 13:29:50.470 -0.109617 -0.857322 -0.508587    20.000000
...                           ...       ...       ...          ...

info [dict]
Filename                 : data/sample.cwa.gz
Filesize(MB)             : 69.4
Device                   : Axivity
DeviceID                 : 13110
ReadErrors               : 0
SampleRate               : 100.0
ReadOK                   : 1
StartTime                : 2014-05-07 13:29:50
EndTime                  : 2014-05-13 09:50:33
NumTicks                 : 51391800
WearTime(days)           : 5.847725231481482
NumInterrupts            : 1
ResampleRate             : 100.0
NumTicksAfterResample    : 25262174
LowpassOK                : 1
LowpassCutoff(Hz)        : 20.0
CalibErrorBefore(mg)     : 82.95806873592024
CalibErrorAfter(mg)      : 4.434966371604519
CalibOK                  : 1
NonwearTime(days)        : 0.0
NumNonwearEpisodes       : 0
...

Custom data format

If you have data in another format (e.g. CSV) and still want to leverage the data processing routines (low-pass filtering, calibration, non-wear detection and resampling), you can do it by converting your data to a Pandas dataframe and using actipy.process.

import actipy

data, info = actipy.process(data, sample_rate=100,
                            lowpass_hz=20,
                            calibrate_gravity=True,
                            detect_nonwear=True,
                            resample_hz=50)

See the API reference.

Fine-tune processing

You can access the individual processing routines at actipy.processing.* for more fine-grained control.

import actipy.processing as P

data, info_calib = P.calibrate_gravity(data, calib_cube=0.2)
data, info_nonwear = P.flag_nonwear(data, patience='2h')

See the API reference.