Data Processing and Management

This section explains how Utah Flux Network data are processed and managed using Python.

Modules

1. `converter.py` - AmerifluxDataProcessor: Parses AmeriFlux TOA5 CSVs into pandas DataFrames. - Reformatter: Cleans, standardizes, and resamples data using timestamp inference, column renaming, and soil sensor logic.

2. `tools.py` - Detects irrigation events (find_irr_dates) - Identifies missing data gaps and visualizes them (find_gaps, plot_gaps) - Flags extreme variations

3. `graphs.py` - energy_sankey(): Visualizes daily energy balance as Sankey diagrams - scatterplot_instrument_comparison(): Compares instruments with regression stats

4. `headers.py` - Utilities for detecting and applying missing headers across files

5. `station_data_pull.py` - Fetches logger data over HTTP from remote stations - Compares and inserts data into SQL databases