tern-stac 0.4.1

Python helpers for working with TERN STAC data

TERN STAC

Python wrapper around pystac-client with TERN STAC defaults and convenience loaders for rasterio, xarray, and geopandas.

Install

pip install tern-stac

Optional dependency extras:

pip install tern-stac[rasterio]
pip install tern-stac[xarray]
pip install tern-stac[geopandas]
pip install tern-stac[odc]
pip install tern-stac[plot]
pip install tern-stac[lidar]
pip install tern-stac[stackstac]
pip install tern-stac[all]

Usage

from tern_stac import TernStacClient
from tern_stac.client import load_from_tern

# Uses the fixed TERN endpoint by default.
client = TernStacClient()
# Or override with an env var:
# export TERN_STAC_URL="https://your.tern.stac.endpoint"

# Search collections/items (STAC API style)
items = client.search(collections=["TERN"])
first_item = next(items.items())

# Load raster asset by key
with client.load_rasterio(first_item, asset_key="rgb") as ds:
    arr = ds.read(1)

# Directly load into xarray (via rioxarray)
xda = client.load_xarray(first_item, asset_key="rgb")

# Directly load vector-style asset into geopandas
gdf = client.load_geodataframe(first_item, asset_key="geometry")

Configure APIkey for accessing TERN data

How to get a TERN API Key

To generate an API Key, please visit the TERN Account portal at (https://account.tern.org.au) and Sign In. After Sign In, follow the steps below (see figure 1 and figure 2):

Steps:

1. In the menu on the left, click Create API key menu link (1)

2. Enter the name of the API key in the API key name field (can be arbitrary, for your records - but it's mandatory) (2)

3. Click the button Request API Key (3)

4. Copy the generated API key in the API key field (4)

How to use the API Key

Reading data via data.tern.org.au involves underlying gdal methods. a gdalrc or netrc file is required for authentication.

(preferred) create netrc config file

~/.netrc is a common way to configure authentication for network services, and is supported by many tools like curl, python requests, gdal and many others.

The config file name is strict .netrc and need to be put under home directory, example:

• windows: "C:\Users\(username)\.netrc"
• linux: "/home/(username)/.netrc"

create ~/.netrc using the apikey:

# .netrc contents to read data from data.tern.org.au
machine data.tern.org.au
  login apikey
  password <apikey>

machine other.sources.if.any
  ...

create gdalrc config file

~/.gdal/gdalrc contains gdal-only env variables

The config file name is strict gdalrc and need to be put under home directory, example:

• windows: "C:\Users\(username)\.gdal\gdalrc"
• linux: "/home/(username)/.gdal/gdalrc"

create ~/.gdal/gdalrc using the apikey:

[credentials]

[.dataprod]
path=/vsicurl/https://data.tern.org.au
GDAL_HTTP_USERPWD=apikey:<apikey>

[.other_sources_if_any]
path=...

Direct helpers

ds = load_from_tern("https://example.com/some.tif", backend="rasterio")

STAC asset helpers

get_item_asset_href(...) now supports multiple matching assets:

• returns a str when exactly one asset matches
• returns a list[str] when multiple assets match filters (media_type/role)

Examples

Run one of the API examples:

python examples/imagery_api_workflow.py
python examples/fractional_cover_api_workflow.py
python examples/lidar_chm_api_workflow.py
python examples/stackstac_api_workflow.py

stackstac_api_workflow.py uses the stackstac gif tutorial data source (Planetary Computer) and requires:

pip install planetary-computer

The first three scripts use the TERN STAC API via TernStacClient. The stackstac script intentionally uses Planetary Computer data from the stackstac tutorial.

from tern_stac import TernStacClient

client = TernStacClient()
search = client.search(collections=["<collection_id>"])
items = list(search.items())
print(len(items))

from tern_stac import get_item_asset_href, load_items_as_time_series, load_items_odc

asset_or_assets = get_item_asset_href(item, media_type="application/xml", role="data")
# `asset_or_assets` is `str` for one match, or `list[str]` for multiple matches.

# Load many rasterio-compatible items into a time-indexed xarray object
# For items containing multiple matching TIFF assets with the same timestamp:
# - point query: samples each tile and averages per timestamp
# - clip_bounds query: uses all intersecting tiles and computes one ROI mean per timestamp
ts = load_items_as_time_series(
    items,
    media_type="application/xml",
    role="data",
    chunks=True,
)

# Load many items with odc-stac (multi-band / grouped loading)
ds = load_items_odc(items, bands=["b5", "b4", "b3"], crs="utm", groupby="solar_day")

stackstac helpers

from tern_stac import (
    load_items_stackstac,
    mosaic_time,
    get_array_bounds,
    get_array_epsg,
)

arr = load_items_stackstac(
    items,
    assets=["b5", "b4", "b3"],
    bounds_latlon=(135.62, -30.67, 135.63, -30.66),
)

print(get_array_epsg(arr))
print(get_array_bounds(arr, to_epsg=4326))

arr_mosaic = mosaic_time(arr)

ROI and reduction helpers

from tern_stac import bounds_from_geodataframe, spatial_slice, mean_over_dims

bounds = bounds_from_geodataframe(region_gdf)

roi = spatial_slice(ds, bounds=bounds)
mean_by_time = mean_over_dims(roi, dims=("x", "y"))

LiDAR helper

from tern_stac import laz_to_canopy_height

chm = laz_to_canopy_height("https://data.tern.org.au/uas/dronescape/..../file.copc.laz")

Quick plotting utilities

from tern_stac import preview_raster, plot_time_series, explore_odc

# Plot a single band/time scene
preview_raster(ds, band=0, save_path="preview.png")

# Plot region-mean time series
plot_time_series(ts, figsize=(12, 4))

# Interactive map via odc.explore (when odc-geo is available)
explore_odc(ds, band=0)

Build and release

GitHub

git init
git add .
git commit -m "Initial TERN STAC wrapper"
git remote add origin https://github.com/<org>/TERN-STAC.git
git push -u origin main

Create a dedicated test branch:

git checkout -b test
git push -u origin test

PyPI (manual)

python -m pip install --upgrade build twine
python -m build
python -m twine check dist/*
python -m twine upload dist/*

GitHub Actions for releases

Tag strategy used by the workflow:

• vX.Y.Z → publishes to PyPI
• test-X.Y.Z → publishes to TestPyPI

Version is now provided automatically from git tags by setuptools_scm; you no longer need to manually edit src/tern_stac/_version.py.

Example for test branch release:

git checkout test
git tag test-0.1.0
git push origin test-0.1.0

Example for production release:

git checkout main
git tag v0.1.0
git push origin v0.1.0

GitHub Actions (included)

The workflow in .github/workflows/publish.yml publishes automatically with OpenID Connect:

• v* tags → PyPI
• test-* tags → TestPyPI

License

MIT