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Source: python-dwdwfsapi
Maintainer: Home Assistant Team <team+homeassistant@tracker.debian.org>
Uploaders:
Edward Betts <edward@4angle.com>,
Section: python
Build-Depends:
debhelper-compat (= 13),
dh-sequence-python3,
pybuild-plugin-pyproject,
python3-all,
python3-hatchling,
Build-Depends-Indep:
python3-pytest <!nocheck>,
python3-requests <!nocheck>,
Standards-Version: 4.7.3
Homepage: https://github.com/stephan192/dwdwfsapi
Vcs-Browser: https://salsa.debian.org/homeassistant-team/deps/python-dwdwfsapi
Vcs-Git: https://salsa.debian.org/homeassistant-team/deps/python-dwdwfsapi.git
Testsuite: autopkgtest-pkg-pybuild
Package: python3-dwdwfsapi
Architecture: all
Depends:
${misc:Depends},
${python3:Depends},
Description: Client for Deutscher Wetterdienst weather warnings and forecast data
This library retrieves weather data published by Deutscher Wetterdienst
through its GeoServer WFS service. It covers three data sets for Germany:
weather warnings, bioweather forecasts, and pollen forecasts.
.
For weather warnings, the library can query DWD warncell data by warncell ID,
warncell name, or geographic coordinates. It provides the selected region
identifier and name, the time of the last update, separate lists of current
warnings and advance warnings, and the highest warning level present in each
group. Individual warning entries include start and end times, event name and
code, official headline, descriptive text, precautionary instructions when
present, urgency, warning parameters, and the warning colour.
.
For bioweather forecasts, the library talks to DWD forecast regions and
returns forecast entries grouped by health-related category. These categories
include general well-being as well as effects associated with asthma,
circulatory conditions, and rheumatic complaints. Forecast entries contain the
forecast start time, an impact level, a textual description of that impact,
and a colour value.
.
For pollen forecasts, the library queries DWD regional forecast data for
several pollen types, including hazel, alder, ash, birch, grasses, rye,
mugwort, and ambrosia. The returned data is grouped by pollen type and
includes dated forecast entries with numeric level, textual impact
description, and colour value.
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