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Wavelet-based Eddy Covariance Written by pedrohenriquecoimbra

Project description

DOI

DOI

Citation

Pedro H H Coimbra, Benjamin Loubet, Olivier Laurent, Matthias Mauder, Bernard Heinesch, Jonathan Bitton, Jeremie Depuydt, Pauline Buysse. Improvement of CO2 Flux Quality Through Wavelet-Based Eddy Covariance: A New Method for Partitioning Respiration and Photosynthesis. http://dx.doi.org/10.2139/ssrn.4642939

* corresponding author: pedro-henrique.herig-coimbra@inrae.fr

Getting started

  1. Setup python.
    (optional) Create python environment, with anaconda prompt run conda create -n wavec
    (optional) Activate new environement, activate wavec
    Install python library, pip install waveletec

  2. Run EddyPro, saving level 6 raw data.
    To do this go in Advanced Settings (top menu) > Output Files (left menu) > Processed raw data (bottom);
    Then select Time series on "level 6 (after time lag compensation)";
    Select all variables;
    Proceed as usual running on "Advanced Mode".

  3. Follow launcher.ipynb

If directly cloning github

  1. Setup python.
    (option 1) install anaconda, and run conda create -n wavec --file requirements.txt
    (option 2) install anaconda, and run conda create -f environment.yml

Example

For an example follow the launcher_sample.ipynb file in folder sample\FR-Gri_20220514.

Supported by

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