Advanced Framework for Sap Flow Data Post-Process
Calculate dTmax by the double regression method
Calculate dTmax by the environmental dependent method
Calculate dTmax by the moving window and double regression methods
Calculate dTmax by the moving window method
Calculate dTmax by the daily predawn and environmental dependent metho...
Calculate dTmax by the daily predawn method
Calculate dTmax by the successive predawn method
Calculate dTmax by various methods
Calculate sap flux density time series
Define reference values of average and standard deviation
Calculate global solar radiation time series at TOA
Remove outliers by absolute limits
Detect periods when short-term signal attenuation occurs
Fill missing values with a random forest model
Filter high frequency noise by Gaussian filter
fluxfixer: Advanced Framework for Sap Flow Data Post-Process
Obtain time interval of input timestamp vector
Modify short-term drifts
Obtain the number of data points without missing values
Pipe operator
Remove error values manually
Remove outliers detected by a random forest model
Remove outliers by Z-score time series
Retrieve time series in its original units
Tune hyperparameters used in a random forest model
Predict targeted time series by a random forest model
Run all quality control processes automatically
Provides a flexible framework for post-processing thermal dissipation sap flow data using statistical methods and machine learning. This framework includes anomaly correction, outlier removal, gap-filling, trend removal, signal damping correction, and sap flux density calculation. The functions in this package can also apply to other time series with various artifacts.
Useful links