Tools for QA/QC of eddy covariance station data
Project description
flux-data-qaqc
flux-data-qaqc provides a framework to create reproducible workflows for the post-processing and analysis of eddy covariance time series data.
Notable uses:
data validation with methods for quality-based filtering
time series tools, e.g. gap-filling and temporal aggregation
energy balance closure algorithms and other meterological calculations
data provenance, e.g. from metadata management and file structure
downloading and management of gridMET meterological data
customizable and interactive visualizations
built-in unit conversions
facilitating batch processing
Documentation
Installation
Using PIP:
pip install fluxdataqaqcPIP should install the necessary dependencies however it is recommended to use conda and first install the provided virtual environment. This is useful to avoid changing your local Python environment. Note, flux-data-qaqc has been tested for Python 3.7+, although it may work with versions greater than or equal to 3.4.
First make sure you have the fluxdataqaqc environment file, you can download it here. Next to install run,
conda env create -f environment.ymlTo activate the environment before using the flux-data-qaqc package run,
conda activate fluxdataqaqcNow install using PIP:
pip install fluxdataqaqcNow all package modules and tools should be available in your Python environment PATH and able to be imported. Note if you did not install the Conda virtual environment above, PIP should install dependencies automatically but be sure to be using a version of Python above or equal to 3.4. To test that everything has installed correctly by opening a Python interpretor or IDE and run the following:
import fluxdataqaqcand
from fluxdataqaqc import Data, QaQc, PlotIf everything has been installed correctly you should get no errors.
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