Getting Started with Micromet

Micromet is a Python package for processing micrometeorological data from environmental monitoring stations. It provides utilities for formatting, validating, and analyzing data from sources such as AmeriFlux and Campbell Scientific systems.

This guide walks you through installation, basic usage, and how to start working with your own data.

Installation

Micromet is available on PyPI and can be installed using pip:

pip install micromet

Alternatively, if you want to use the latest development version, you can install it directly from GitHub:

pip install git+https://github.com/inkenbrandt/MicroMet.git

MicroMet is also available as a conda package. You can install it using the following command:

conda install -c conda-forge micromet

Micromet can be installed from source. First, clone the repository:

git clone https://github.com/inkenbrandt/MicroMet.git
cd MicroMet

It’s recommended to use a virtual environment:

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

Install the package with optional documentation and development dependencies:

pip install -e .[docs,test]

Usage Overview

Micromet consists of modular tools for reformatting and analyzing micrometeorological data. Here’s a basic example to get started:

from micromet.format.reformatter import Reformatter

# Load your raw data into a pandas DataFrame
import pandas as pd
df = pd.read_csv("path/to/your/data.csv")

# Create a Reformatter instance and process the data
ref = Reformatter()
cleaned_df, report = ref.prepare(df, data_type='eddy')

# Now cleaned_df contains the cleaned and normalized data
print(cleaned_df.head())

Modules

The key subpackages and modules in Micromet are:

  • format — Data formatting subpackage:

    • reformatter — Main Reformatter class for cleaning and standardizing data

    • file_compile — Utilities for compiling multiple raw .dat files into a single dataset

    • merge — Functions for merging eddy covariance and met data streams

    • compare — Instrument comparison and cross-correlation functions

    • headers — Utilities for detecting and applying missing headers across files

    • transformers — Data transformation submodule (columns, timestamps, corrections, validation, cleanup, interval_updates)

  • qaqc — Quality assurance and control subpackage:

    • variable_limits — Physical and plausible range definitions for all variables

    • netrad_limits — Net radiation QA/QC and timestamp alignment

    • data_cleaning — QC flag application and data cleaning

  • report — Reporting and visualization subpackage:

    • graphs — Plotting functions (energy Sankey diagrams, instrument comparison scatter plots)

    • tools — Utility functions for analysis (irrigation event detection, gap finding)

    • validate — Data validation, lag detection, and sensor intercomparison

    • fix_g_values — Soil heat flux storage corrections

    • recalculate_albedo — Albedo recalculation utilities

    • gap_summary — Gap analysis and reporting

    • eddy_plots — Eddy covariance diagnostic plots

    • easyflux_footprint — Flux footprint analysis

    • alfalfa_growth — Alfalfa height modeling from growing degree days

  • reader — AmerifluxDataProcessor for reading AmeriFlux and TOA5 data files

  • station_data_pull — StationDataDownloader and StationDataProcessor for managing station data

Note

The notebooks/ directory contains worked examples and is not part of the core API. See Example Notebooks for a guide to the available notebooks.

Contributing

We welcome contributions! If you have suggestions, bug reports, or would like to add features:

  1. Fork the repository

  2. Create a new branch

  3. Submit a pull request

Please make sure to add unit tests for new functionality and follow PEP8 standards.

Further Reading