flux-data-qaqc
provides a framework to create reproducible workflows for the analysis of eddy covariance time series data. In particular for performing energy balance closure analysis and correction routines which adjust turbulent fluxes.
Notable tools include:
- data validation with methods for quality-based filtering
- time series data tools, e.g. temporal aggregation and resampling
- management of site metadata, data provenance, and file structure
- energy balance closure algorithms and other meterological calculations
- downloading and management of gridMET meterological data
- customizable and interactive data visualizations
- batch processing
- unit conversions
Currently clone or download from GitHub,
$ git clone https://github.com/Open-ET/flux-data-qaqc.git
Optionally, as opposed to using PIP, you may install dependencies with the provided Conda 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.yml
To activate the environment before using the flux-data-qaqc
package run,
$ conda activate fluxdataqaqc
Run the following to install flux-data-qaqc
in developer mode, soon the package will be uploaded and available on PYPI,
$ cd flux-data-qaqc
$ pip install -e .
Now 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 fluxdataqaqc
and
from fluxdataqaqc import Data, QaQc, Plot
If everything has been installed correctly you should get no errors.