read_GHG

Functions for reading and processing LI-COR GHG high-frequency data files.

This module provides functionality to read and process high-frequency data from LI-COR GHG files, specifically focusing on the SMARTFLUX system output. It includes tools for:

  • Reading and extracting data from zipped GHG files

  • Processing diagnostic values from LI-7200 gas analyzer

  • Handling AGC (Automatic Gain Control) values and other diagnostic flags

Author: Ariane Faures Created: October 5, 2021

Functions

read_GHG

Read and extract high-frequency data from LI-COR SMARTFLUX GHG files.

read_diag_val

Process LI-7200 gas analyzer diagnostic values.
read_GHG.read_GHG(raw_file, raw_format='ghg', unzip_path=None)[source]

Read and extract high-frequency data from LI-COR SMARTFLUX GHG files.

This function handles the reading of high-frequency eddy covariance data from LI-COR SMARTFLUX GHG files. It extracts both data and metadata from zipped GHG files and returns them as pandas DataFrames.

Parameters:

  • raw_file (str) – Path to the GHG file to process

  • raw_format (str, optional) – Format of the raw data file, currently only ‘ghg’ is supported

  • unzip_path (str, optional) – Directory where the GHG file should be temporarily extracted. If None, uses the same directory as the GHG file

Returns:

A list containing:

  • file_header : pandas.DataFrame Header information from the data file (first 6 lines)

  • file_data : pandas.DataFrame High frequency data with variable names as columns

  • data_name : str Path to the extracted data file

  • metadata_name : str Path to the extracted metadata file

Return type:

list

Notes

The function automatically cleans up extracted files after reading them.

read_GHG.read_diag_val(data)[source]

Process LI-7200 gas analyzer diagnostic values.

This function processes diagnostic values from a LI-7200 gas analyzer, converting binary diagnostic flags into meaningful status indicators. Each diagnostic value is a binary number where specific bits indicate the status of different analyzer components.

Parameters:

data (pandas.Series or numpy.ndarray) – Series or array of diagnostic values from the analyzer

Returns:

DataFrame containing diagnostic information for each component:

AGCfloat

Mean AGC value (Automatic Gain Control, scaled by 6.67)

Syncint

Count of synchronization issues

PLLint

Count of Phase-Locked Loop issues

Detectorint

Count of detector issues

Chopperint

Count of chopper wheel issues

DeltaPressureint

Count of pressure differential issues

Aux_inputint

Count of auxiliary input issues

Tinletint

Count of inlet temperature issues

Toutletint

Count of outlet temperature issues

Head detectint

Count of head detection issues

Return type:

pandas.DataFrame