Example Notebooks

The docs/notebooks/ directory contains Jupyter Notebooks that demonstrate the MicroMet processing workflow and package capabilities. Each notebook corresponds to a stage of the end-to-end pipeline described in Flux Data Processing – Workflow Summary.

Note

Notebooks are provided for reference and are not re-executed during documentation builds. Data files required by some notebooks (large CSVs, station .dat files) are not included in the repository.

Processing Workflow Notebooks

These notebooks implement the numbered processing steps for a flux station (see Eddy Covariance Flux Data Processing Workflow for full technical details).

Notebook	Description
`cs_files.ipynb`	Reads and compiles Campbell Scientific `.dat` files; demonstrates `file_compile` and `Reformatter.preprocess()` for CS-format eddy data (workflow Step 1).
`Appending Data From Dataloggers.ipynb`	Merges data downloaded directly from dataloggers with compiled station archives; covers gap-filling and timestamp alignment (workflow Steps 1–2).
`netrad_limits_getting_started.ipynb`	Applies net radiation QA/QC and physical limit checks using `qaqc.netrad_limits` and `Reformatter.finalize()` (workflow Step 3).
`Sensor Comparisons.ipynb`	Side-by-side instrument intercomparison using `report.validate` and `report.graphs.scatterplot_instrument_comparison()` (workflow Step 3a).
`DL_forAmeriFluxOutputOnly.ipynb`	Exports a quality-controlled dataset to AmeriFlux-formatted CSV: signal-strength filtering, column dropping, timestamp formatting, and −9999 fill (workflow Step 4).
`footprint_recalculation.ipynb`	Estimates and maps the flux footprint from EasyFlux outputs using `report.easyflux_footprint` (supplemental analysis).

Analysis and Utility Notebooks

Notebook	Description
`converter_tutorial (2).ipynb`	Introductory tutorial for the `Reformatter` class: loading raw data, running `prepare()`, and inspecting the QC report.
`graphs_usage_example.ipynb`	Examples of all major plotting functions in `report.graphs`, including energy Sankey diagrams and instrument comparison scatter plots.
`fix_headers.ipynb`	Demonstrates `format.headers` utilities for detecting and applying missing column headers across a set of `.dat` files.
`Pull DB Data and Impute.ipynb`	Retrieves gap-filled reference data from the UGS database and performs meteorological variable imputation.
`alfalfa_height.ipynb`	Simulates alfalfa canopy height using growing degree days via `report.alfalfa_growth.simulate_alfalfa_height_multi_field()`, calibrated with experimental data from Wellington, UT (January 2023 – December 2024).