terndata.ecoplots 1.1.3.dev14

Python client for discovering, filtering, and retrieving ecological field data from the TERN EcoPlots Portal. Pagination, streaming, and response normalisation are handled automatically — you get back a clean pandas.DataFrame, geopandas.GeoDataFrame, GeoJSON (for observations), or Parquet output with no post-processing required.

Features

Data access

• 🔬 Observations & Samples — Two purpose-built workflows covering ecological plot observations and physical material samples (soil, plant vouchers, tissue) from TERN's national monitoring network.
• 🌐 Full API abstraction — Pagination, streaming, and response normalisation are handled automatically; you work with clean Python objects, not raw HTTP.
• 📦 Analysis-ready outputs — Observations and samples return a geopandas.GeoDataFrame by default; also available as pandas.DataFrame or Parquet bytes. Observations can also be returned as GeoJSON.

Discovery & filtering

• 🔎 Validated filters with fuzzy resolution — Mistyped or partial filter values are caught and corrected before any request is sent, with a ranked list of suggestions.
• ⚡ Preview before you download — Inspect the first page of results instantly to confirm your filters before committing to a full retrieval.
• 🗺️ Interactive spatial selector — Draw a bounding polygon on a live map widget directly in Jupyter; the geometry is applied as a filter automatically.

Developer experience

• 🧭 Sync and async clients — EcoPlots for scripts and notebooks; AsyncEcoPlots for async/ASGI services and concurrent I/O pipelines.
• 💾 Reproducible projects — Save and reload the full discovery state as a .ecoproj file for shareable, version-controllable workflows.
• 🖼️ Notebook widgets — Built-in IGSN viewer and sample image browser for interactive exploration without leaving the notebook.

📖 Full documentation: https://terndata-ecoplots.readthedocs.io/en/latest/

Installation

pip install terndata.ecoplots

The standard install includes the synchronous client, observations and samples workflows, discovery/filtering, data retrieval, attribute data retrieval, and Parquet output.

Optional modules:

pip install "terndata.ecoplots[async]"  # AsyncEcoPlots and async streaming transport
pip install "terndata.ecoplots[gui]"    # Jupyter/ipyleaflet/ipywidgets helpers

Supported Python: 3.10, 3.11, 3.12, 3.13 Core dependencies include geopandas, pandas, pyarrow, requests, rapidfuzz, and orjson. Async and GUI dependencies are installed only when their extras are requested.

Quick Start

Zero to data in three lines:

from terndata.ecoplots import EcoPlots

ec = EcoPlots()
ec.select(site_id="TCFTNS0002")
gdf = ec.get_data()          # → GeoDataFrame, ready for analysis

See Modes for full workflow examples, or jump to the demo notebooks.

Modes

Observations (default)

Retrieve ecological observation data — site visits, feature types, and measured properties — across Australia's TERN monitoring network.

from terndata.ecoplots import EcoPlots

ec = EcoPlots()                           # mode="observations" by default
ec.select(dataset="TERN Surveillance",
          site_id="TCFTNS0002")
ec.preview()                              # quick look (first page)
gdf = ec.get_data()                       # default → GeoDataFrame
df  = ec.get_data(dformat="pd")           # → pandas DataFrame
pq  = ec.get_data(dformat="pq")           # → Parquet bytes
gjson = ec.get_data(dformat="geojson")    # → GeoJSON (observations only)
ec.export_data("outputs/ecoplots.parquet") # retrieve and save directly
sites = ec.get_sites(include_region=True) # site region columns included
site_attrs = ec.get_site_attributes_data()

Samples

Retrieve physical specimens — soil pit samples, plant voucher specimens, plant tissue samples, and more — with access to IGSN identifiers and sample images.

from terndata.ecoplots import EcoPlots

ec = EcoPlots("samples")
ec.select(material_sample_type="Plant Voucher Specimen",
          has_image=True)
gdf = ec.get_data()                        # default → GeoDataFrame
pq = ec.get_data(dformat="pq")             # Parquet bytes
ec.export_data("outputs/samples.csv")      # retrieve and save directly
sites = ec.get_sites(include_region=True)  # enrich sites with region columns

Samples mode includes two dedicated notebook widgets:

Note: In samples mode the TERN Ecosystem Surveillance dataset is applied automatically and cannot be removed.

Async client (AsyncEcoPlots)

For async/ASGI services or concurrent I/O pipelines, install the async extra and use AsyncEcoPlots — it has the same interface as EcoPlots with await-able retrieval methods. Both modes and all filters are supported.

from terndata.ecoplots import AsyncEcoPlots

ec = AsyncEcoPlots()
ec.select(site_id="TCFTNS0002")
gdf = await ec.get_data()        # non-blocking fetch

async for chunk in ec.get_data_stream(dformat="gpd"):
    ...

Interactive Widgets

Both modes provide notebook widgets for interactive data exploration:

Demo Notebooks

Links

• 📚 Docs: https://terndata-ecoplots.readthedocs.io/en/latest/
• 🗺️ Workflow Reference: https://terndata-ecoplots.readthedocs.io/en/latest/workflows.html
• 🧭 EcoPlots Portal: https://ecoplots.tern.org.au
• 🧑‍💻 Source: https://github.com/ternaustralia/terndata.ecoplots
• 📦 PyPI: https://pypi.org/project/terndata.ecoplots/

Contributing

Contributions are welcome! Please open an issue first to discuss substantial changes before submitting a PR. For small bug fixes, a direct PR is fine.

Target branch: main

Development commands:

All checks are also available via tox — see tox.ini for environment definitions.

Support

• 🐛 Bug reports & feature requests: Open a GitHub issue
• 📧 Direct enquiries: esupport@tern.org.au
• 📚 Documentation: terndata-ecoplots.readthedocs.io

Citation

Terrestrial Ecosystem Research Network (2026). terndata.ecoplots: A Python package for accessing TERN EcoPlots data. https://pypi.org/project/terndata.ecoplots/

For citation metadata, see CITATION.cff. This cites the software tool itself; data accessed through EcoPlots may require separate dataset-specific citation and attribution.

License

Licensed under the terms in LICENSE. Copyright © 2025-2026 TDSA (TERN Data Services and Analytics). Author: Avinash Chandra