fluxdataqaqc 0.1.3

Tools for QA/QC of eddy covariance station data

flux-data-qaqc

flux-data-qaqc provides a framework to create reproducible workflows for the validation and analysis of eddy covariance time series data.

Notable uses:

• data validation with methods for quality-based filtering

• time series tools, e.g. temporal aggregation, interpolation, and resampling

• 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

ReadTheDocs

Installation

Using PIP:

pip install fluxdataqaqc

PIP 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.yml

To activate the environment before using the flux-data-qaqc package run,

conda activate fluxdataqaqc

Now install using PIP:

pip install fluxdataqaqc

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.