Axivity Outdoor Light Exposure Analysis

Install

Minimum requirements: Python 3.8 to 3.10, Java 8 (1.8)

The following instructions make use of Anaconda to meet the minimum requirements:

1. Download & install Miniconda (light-weight version of Anaconda).
2. (Windows) Once installed, launch the Anaconda Prompt.
3. Create a virtual environment (here named axlux):

   $ conda create -n axlux python=3.9 openjdk pip

   This creates a virtual environment called axlux with Python version 3.9, OpenJDK, and Pip.
4. Activate the environment:

   $ conda activate axlux

   You should now see (axlux) written in front of your prompt.
5. Install the axivity-outdoor-light package:
   • Method 1 (SSH):

   $ pip install 'axivity-outdoor-light @ git+ssh://git@github.com/OxWearables/axivity-outdoor-light'

   • Method 2 (HTTPS):

   $ pip install 'axivity-outdoor-light @ git+https://github.com/OxWearables/axivity-outdoor-light'

You are all set! The next time that you want to use axlux, open the Anaconda Prompt and activate the environment (step 4). If you see (axlux) in front of your prompt, you are ready to go!

Usage

# Process an AX3 file
$ axlux sample.cwa

# Or an ActiGraph file
$ axlux sample.gt3x

# Or a GENEActiv file
$ axlux sample.bin

# Or a CSV file (see accepted data format below)
$ axlux sample.csv

Output:

Summary
-------
{
    "Filename": "sample.cwa",
    "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",
    "TotalOutdoorLight(mins)": 492.0,
    "OutdoorLightDayAvg(mins)": 70.28571428571429,
    "OutdoorLightDayMed(mins)": 51.0,
    "OutdoorLightDayMin(mins)": 12.0,
    "OutdoorLightDayMax(mins)": 165.0,
    ...
}

Estimated Daily Outdoor Light
-----------------------------
            OutdoorLight(mins)  OutdoorLightAdjusted(mins)
time
2014-05-07            51.0               63.166667
2014-05-08            39.0               39.000000
2014-05-09            68.0               68.000000
2014-05-10           120.0              120.000000
...

Output: outputs/sample/

Troubleshooting

Some systems may face issues with Java when running the script. If this is your case, try fixing OpenJDK to version 8:

$ conda install -n axlux openjdk=8

Output files

By default, output files will be stored in a folder named after the input file, outputs/{filename}/, created in the current working directory. You can change the output path with the -o flag:

$ axlux sample.cwa -o /path/to/some/folder/

The following output files are created:

• Info.json Summary info, as shown above.
• Minutes.csv Raw time-series of outdoor light minute-by-minute estimates.
• Hourly.csv Hourly outdoor light time.
• Daily.csv Daily outdoor light time.
• HourlyAdjusted.csv Like Hourly but accounting for missing data (see section below).
• DailyAdjusted.csv Like Daily but accounting for missing data (see section below).

Crude vs. Adjusted Estimates

Adjusted estimates are provided that account for missing data. Missing values in the time-series are imputed with the mean of the same timepoint of other available days. For adjusted totals and daily statistics, 24h multiples are needed and will be imputed if necessary. Estimates will be NaN where data is still missing after imputation.

Processing CSV files

TODO

Licence

TODO

Acknowledgements

We would like to thank all our code contributors, manuscript co-authors, and research participants for their help in making this work possible.