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