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Dijkhof 1 year ago
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README.md

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In this folder you can find 4 scripts that can be used to transfrom 24/7 .cwa accelerometer data to intensity plots. The methods are based on the Rowland et al. 2018 article 'Beyond Cut Points: Accelerometer Metrics that Capture the Physical Activity Profile.
In this folder you can find 4 scripts that can be used to transfrom 24/7 .csv accelerometer data to intensity plots. The methods are based on the Rowland et al. 2018 article 'Beyond Cut Points: Accelerometer Metrics that Capture the Physical Activity Profile.
The scripts should be run in the following order:
1. DailyCutter.py - ...
1. DailyCutter.py - This script is used to transform single week .csv files (input) into individual day-files (output)
The WeekPath should be set to the folder containing the week-files of the patient
The ScriptPath should be set to the folder containing the 'formules.py' script. This script holds
all functions used in the analysis.
2. plotter.py - This is the second script used in the analysis and is the script that performs the most.
Before getting started, the os.chdir(___) should be set to the 'formules.py' pathway
The rootdir should be set to the folder containing 7 individual .csv day-files
The PtName variable should be set accordingly to the name of the files used. This possibly changes
between different weeks/different patients.
Finally, the CheckWeel.to_csv(___) should be filled with the used weeknumber, to save the
parameters of the trendline for each different week.
After changing these variables to your liking, the script should run automatically, using the
folder with individual days as input and saving the plots for individual days as well as an
overview of the entire week as output.
2. plotter.py - ...
3. Totalplotter.py - After running the plotter.py script you should have ended up with 6 .csv files with parameters
of the trendlines. Make sure that these files are saved within the same folder and set the path
of this folder as the rootdir in this script.
Change the names in the plt.title(___) and plt.savefig(___) commands accordingly and run the scripts
The output will be a plot of all 6 weeks in one figure.
3. Totalplotter.py - ...

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