waveletec 0.3.0.0.2

Wavelet-based Eddy Covariance Written by pedrohenriquecoimbra

WaveletEC Module

WaveletEC is a Python module designed for processing and analyzing Eddy Covariance data using wavelet transforms. This module provides functionalities to handle EddyPro setup files, perform wavelet analysis, and integrate results.

Citation

Pedro H H Coimbra, Benjamin Loubet, Olivier Laurent, Matthias Mauder, Bernard Heinesch, Jonathan Bitton, Nicolas Delpierre, Daniel Berveiller, Jérémie Depuydt, Pauline Buysse. Evaluation of a novel approach to partitioning respiration and photosynthesis using eddy covariance, wavelets and conditional sampling. https://doi.org/10.1016/j.agrformet.2025.110684

* corresponding author: [e-mail]

Installation

From pip

To install the WaveletEC module, use pip:

Recommended step (optional):

conda create -n wavec
conda activate wavec

Install library:

pip install waveletec

From git

To install the WaveletEC module, clone the repository and install the required dependencies:

git clone https://github.com/pedrohenriquecoimbra/wavelet-ec
cd waveletec

Then one of the follwoing options:

• pip install -r requirements.txt
• conda create -n wavec --file requirements.txt
• conda create -f environment.yml

Usage

(Recommended) Using EddyPro

1. Make sure EddyPro® is installed.
2. Run EddyPro, saving level 6 raw data.
   • go to Advanced Settings (top menu) > Output Files (left menu) > Processed raw data (bottom);
   • select Time series on "level 6 (after time lag compensation)";
   • select all variables;
   • proceed as usual running on "Advanced Mode".
3. Run waveletec:
   This requires pip install waveletec

   import waveletec
   waveletec.run_from_eddypro("PATH/TO/EDDYPRO/SETUP", ...):
   

   Or directly in the command line:

   cd PATH/TO/WAVELETEC/LIBRARY
   python -m waveletec.handler --help
   python -m waveletec.handler [-args...]
   

Running waveletec

This requires pip install waveletec.

import waveletec

Then

waveletec.process(...)

or directly run from a DataFrame or dictionary:

data = ...
waveletec.main(data, ...)

Saving Results

If you're using waveletec.process, just pass the output_folderpath parameter like this: waveletec.process(..., output_folderpath='PATH/TO/OUTPUT/FOLDER'). This will create a new subfolder called 'wavelet_full_cospectra' for you, and you'll find the sum of all your files neatly stored in the output_folderpath you specified.

Prefer using waveletec.main? No problem! Just pass the output_kwargs parameter like this: waveletec.main(data, ..., output_kwargs={'output_path': 'PATH/TO/OUTPUT/FOLDER'}).

Output Format

The output file for the wavelet-based (co)spectra analysis is structured as follows:

1   wavelet_based_(co)spectra
2   --------------------------------------------------------------
3   TIMESTAMP_START = 2022-05-13 00:00:00
4   TIMESTAMP_END = 2022-05-13 00:30:00
5   N: 133
6   TIME_BUFFER [min] = nan
7   frequency [Hz]
8   y-axis -> nan
9   mother_wavelet -> dwt
10  acquisition_frequency [Hz] = 20.0
11  averaging_interval [Min] = 30min
12  natural_frequency,variable,value
13  3.814697265625e-05,co2,417.0002460141172
.   ...,...,...
.   5.0,co2,1.708199383374261e-05
.   10.0,co2,1.7947312058017124e-07
.   ...,...,...

Explanation of Fields:

• wavelet_based_(co)spectra: Indicates that the data pertains to wavelet-based (co)spectra analysis.

• TIMESTAMP_START and TIMESTAMP_END: These fields specify the start and end times of the data collection period.

• N: Represents the number of data points or samples included in the analysis.

• TIME_BUFFER [min]: Indicates the time buffer in minutes. A value of 0 means no buffer was applied.

• frequency [Hz]: Specifies the frequency of the data is in Hertz.

• y-axis_->_wavelet_coefficient_*_: Indicates that the y-axis represents the wavelet coefficients.

• mother_wavelet -> dwt: Specifies the type of mother wavelet used in the analysis, in this case, dwt (Discrete Wavelet Transform).

• acquisition_frequency [Hz]: The frequency at which data was acquired, given in Hertz.

• averaging_interval [Min]: The interval over which the data was averaged, specified in minutes.

• natural_frequency,variable,value: This line contains the actual data points:

  • natural_frequency: The frequency at which the data point was recorded.
  • variable: The variable being measured, such as co2.
  • value: The value of the variable at the specified natural frequency.

Example

For an example follow the launcher_sample.ipynb file in sample.

License

This project is licensed under the MIT License. See the LICENSE file for details.