diive is currently being prepared for the v1.0 release.

Time series data processing

diive is a Python library for time series processing, focused on ecosystem data. It was originally developed by the ETH Grassland Sciences group for Swiss FluxNet: eddy covariance flux processing, gap-filling, quality control, and the plots that go with them.

There are two ways to use it, and you can pick whichever fits:

• a library — import diive as dv, ten domain namespaces
• a desktop GUI — diive-gui, for interactive work without writing code

diive works on averaged (e.g. 30-minute) data. For raw high-frequency (10/20 Hz) eddy covariance data — wind rotation, flux detection limit, time-lag detection and removal — see dyco.

Documentation | Project overview | Examples | GUI manual | CHANGELOG | Releases

Install

Requires Python 3.12 or 3.13.

pip install diive                 # core library
pip install 'diive[gui]'          # + desktop GUI, then launch with: diive-gui
pip install 'diive[gui,gui3d]'    # + 3-D surface views (PyVista/VTK)
pip install 'diive[db]'           # + InfluxDB read/write
pip install 'diive[gui,gui3d,db]' # all of the above

From a clone, with uv

uv sync                              # core library + the 'dev' group (synced by default)
uv sync --all-extras --all-groups    # everything: all extras AND all groups

uv sync --all-extras alone is not everything. The optional pieces are split across two uv mechanisms, and --all-extras reaches only the first:

db is deliberately both. Working on diive, use the group — uv sync --group db. Depending on diive from another project, ask for the extra — diive[db] — because a dependency group is local to the project that declares it and never reaches the published metadata.

Combine as needed, e.g. uv sync --extra gui --extra gui3d --group db. Then run anything through uv run:

uv run pytest tests/ -v
uv run diive-gui

Standalone Windows app

To build diive-gui.exe for users without Python, run .\packaging\build_gui.ps1 -Clean in PowerShell after installing the build group. Details: packaging/README.md.

CONTRIBUTING.md has the rest of the development setup.

Quick start

import diive as dv

df = dv.load_exampledata_parquet()  # bundled multi-year 30-min eddy covariance record

dv.plotting.TimeSeries(series=df['NEE_CUT_REF_f']).plot()
dv.plotting.HeatmapDateTime(series=df['NEE_CUT_REF_f']).plot()

Plots follow a two-phase pattern throughout: the constructor takes the data, .plot() takes the styling.

From here, the cookbook walks through six minimal workflows — load data, clean timestamps, remove outliers, gap-fill, run the flux chain, visualize.

What's in it

import diive as dv exposes ten domain namespaces. Each row links to runnable examples for that area:

I/O helpers are top-level (dv.load_parquet, dv.save_parquet, dv.ReadFileType) — see io/. For the authoritative symbol list, check diive.__all__ and each namespace's __all__.

Highlights

Flux processing chain — post-processing from quality flags through gap-filling and NEE partitioning (Levels 2 to 4.2), following Swiss FluxNet standards. Either run_chain(data, config) for the standard workflow, or composable per-level callables when you need every detector, hyperparameter and diagnostic flag. → examples/flux/fluxprocessingchain/

NEE partitioning — four faithful ports of the reference routines, each validated against its original implementation: nighttime and daytime (Reichstein 2005, Lasslop 2010) × ONEFlux and REddyProc. Output columns are tagged so all four coexist in one dataframe. → examples/flux/partitioning/

Gap-filling — Random Forest and XGBoost with SHAP-based feature reduction, plus a faithful MDS port that needs no training. An 8-stage feature engineer feeds them all. → examples/gapfilling/

Desktop GUI — the same library code behind an interactive app: plotting, cleaning, gap-filling and flux tabs, a guided processing chain, per-variable metadata with full provenance, and portable .diive project folders. → GUI manual

The Overview tab in diive-gui v0.91.0, showing ten years of half-hourly data. Selecting a variable on the left redraws every panel; zooming one of the date panels recomputes stats and panels for the visible range.

Documentation

Examples run as plain scripts:

uv run python examples/visualization/plot_heatmap_datetime_basic.py
uv run python examples/gapfilling/gapfill_randomforest.py
uv run python examples/flux/fluxprocessingchain/fluxprocessingchain_composable.py

Citation

Cite diive using DOI 10.5281/zenodo.10884017. This concept DOI resolves to the latest release, so include the version number in your citation.

@software{diive2026,
  author = {Hörtnagl, Lukas},
  title = {diive: Python library for time series processing},
  version = {0.91.2},
  year = {2026},
  doi = {10.5281/zenodo.10884017}
}

Replace version and year with the values for your target release.

License

diive is released under the GNU General Public License v3.0.