SGU Client

A modern Python client library for accessing Geological Survey of Sweden (SGU) groundwater data APIs with type safety and pandas integration.

This package is not affiliated with or endorsed by SGU.

Features

• Type-safe: Full type hints with Pydantic validation
• Pandas integration: Convert data to DataFrames or Series with optional pandas dependency
• Friendly shortcuts: Convenience functions to access stations by names, modeled levels by coordinates, and much more.

Get going with your analysis in just a few lines of code:

from sgu_client import SGUClient

with SGUClient() as client:
    measurements = client.levels.observed.get_measurements_by_name(station_id="95_2")
    df = measurements.to_dataframe()

# ... rest of your awesome workflow

Installation

Using uv

# basic installation
uv add sgu-client

# with pandas support for dataframe conversion
uv add "sgu-client[recommended]"

... or using pip

# basic installation
pip install sgu-client

# with pandas support for dataframe conversion
pip install "sgu-client[recommended]"

Usage

Initializing a client

from sgu_client import SGUClient

client = SGUClient()

# with custom configuration
from sgu_client import SGUConfig
config = SGUConfig(timeout=60, debug=True, max_retries=5)
client = SGUClient(config=config)

# as a context manager
with SGUClient() as client:
    stations = client.levels.observed.get_stations(limit=10)

Observed groundwater levels

Access real-time and historical groundwater monitoring data from SGU's network of observation stations.

from sgu_client import SGUClient
from datetime import UTC, datetime


with SGUClient() as client:
    # basic API endpoint usage
    stations = client.levels.observed.get_stations(limit=50)
    
    # with OGC API filters, like bbox of southern Sweden
    stations = client.levels.observed.get_stations(
        bbox=[12.0, 55.0, 16.0, 58.0],
        limit=100
    )

    # or filter expressions
    stations = client.levels.observed.get_stations(
        bbox=[12.0, 55.0, 16.0, 58.0],
        filter_expr="akvifer='JS'",
        limit=100
    )
    
    # convenience function to get station by name
    station = client.levels.observed.get_station_by_name(
        station_id="95_2"  # or station_name="Lagga_2"
    )
    # or multiple stations by names
    stations = client.levels.observed.get_stations_by_names(
        station_id=["95_2", "101_1"]  # or station_name=["Lagga_2", ...]
    )

    # convenience function to get measurements by station name
    measurements = client.levels.observed.get_measurements_by_name(
        station_id="95_2",  # or station_name="Lagga_2"
        limit=100
    )
    # or multiple stations by names
    measurements = client.levels.observed.get_measurements_by_names(
        station_id=["95_2", "101_1"],  # or station_name=["Lagga_2", ...]
        limit=100
    )

    # filter by datetime for faster responses
    tmin = datetime(2020, 1, 1, tzinfo=UTC)
    tmax = datetime(2021, 1, 1, tzinfo=UTC)
    measurements = client.levels.observed.get_measurements_by_names(
        station_id=["95_2"], tmin=tmin, tmax=tmax, limit=10
    )
    
    # responses that create lists of features can be converted to pandas DataFrames
    measurements = client.levels.observed.get_measurements_by_names(
        station_id=["95_2", "101_1"],  # or station_name=["Lagga_2", ...]
        limit=100
    )
    df = measurements.to_dataframe()  
    # or series
    series = measurements.to_series()  # defaults to 'water_level_masl_m' (head) column with datetime index

Modeled groundwater levels

Access modeled groundwater levels from SGU-HYPE.

from sgu_client import SGUClient

with SGUClient() as client:
    # basic API endpoint usage
    areas = client.levels.modeled.get_areas(limit=10)
    
    # with OGC API filters, like bbox of southern Sweden
    areas = client.levels.modeled.get_areas(
        bbox=[12.0, 55.0, 16.0, 58.0],
        limit=20
    )
    
    # get a specific area by ID
    area = client.levels.modeled.get_area("omraden.30125")
    print(f"Area ID: {area.properties.area_id}")
    print(f"Geometry: {area.geometry.type}")
    
    # convenience function to get levels for a specific area
    levels = client.levels.modeled.get_levels_by_area(30125, limit=10)
    # or multiple areas by IDs
    levels = client.levels.modeled.get_levels_by_areas(
        area_ids=[30125, 30126],
        limit=50
    )

    # convenience function to get levels by coordinates
    levels = client.levels.modeled.get_levels_by_coords(
        lat=57.7089,
        lon=11.9746,
        limit=10
    )
    
    # responses that create lists of features can be converted to pandas DataFrames
    levels = client.levels.modeled.get_levels_by_areas(
        area_ids=[30125, 30126],
        limit=50
    )
    df = levels.to_dataframe()
    # or series
    series = levels.to_series()  # defaults to 'relative_level_small_resources' column with datetime index

Groundwater Chemistry

Access groundwater chemistry data including sampling sites and chemical analysis results.

from sgu_client import SGUClient
from datetime import UTC, datetime

with SGUClient() as client:
    # basic API endpoint usage
    sites = client.chemistry.get_sampling_sites(limit=50)

    # with OGC API filters, like bbox of southern Sweden
    sites = client.chemistry.get_sampling_sites(
        bbox=[12.0, 55.0, 16.0, 58.0],
        limit=100
    )

    # and filter expressions
    sites = client.chemistry.get_sampling_sites(
        bbox=[12.0, 55.0, 16.0, 58.0],
        filter_expr="provplatstyp='jordbrunn'",
        limit=100
    )

    # convenience function to get site by name
    site = client.chemistry.get_sampling_site_by_name(
        site_id="10001_1"  # or site_name="Ringarum_1"
    )
    # or multiple sites by names
    sites = client.chemistry.get_sampling_sites_by_names(
        site_id=["10001_1", "10002_1"]  # or site_name=["Ringarum_1", ...]
    )

    # get chemical analysis results
    results = client.chemistry.get_analysis_results(limit=100)

    # convenience function to get results for a specific site
    results = client.chemistry.get_results_by_site(
        site_id="1000_1",  # or site_name="Ringarum_1"
        limit=100
    )
    # or multiple sites by names
    results = client.chemistry.get_results_by_sites(
        site_id=["1000_1", "1001_2"],  # or site_name=["Ringarum_1", ...]
        limit=100
    )

    # filter by datetime for faster responses
    tmin = datetime(2020, 1, 1, tzinfo=UTC)
    tmax = datetime(2021, 1, 1, tzinfo=UTC)
    results = client.chemistry.get_results_by_site(
        station_id="1000_1", tmin=tmin, tmax=tmax, limit=500
    )

    # filter by chemical parameter
    ph_results = client.chemistry.get_results_by_parameter(
        parameter="PH",
        station_id="1000_1",
        tmin=tmin,
        tmax=tmax
    )

    # responses that create lists of features can be converted to pandas DataFrames
    results = client.chemistry.get_results_by_site(
        station_id="1000_1",
        limit=1000
    )
    df = results.to_dataframe()
    # or series
    series = results.to_series()  # defaults to 'measurement_value' column with sampling_date index
    # or pivot by parameter for multi-parameter analysis
    df_pivot = results.pivot_by_parameter()  # creates columns like 'PH', 'NITRAT', 'KLORID', etc.

Working with Typed Data

All responses are fully typed with Pydantic models:

from sgu_client import SGUClient

client = SGUClient()

# Get stations with full type safety
stations = client.levels.observed.get_stations(limit=5)

for station in stations.features:
    # All properties are typed and documented
    print(f"Station: {station.properties.station_name}")
    print(f"Municipality: {station.properties.municipality}")
    print(f"Coordinates: {station.geometry.coordinates}")

# Get measurements with automatic datetime parsing
measurements = client.levels.observed.get_measurements(limit=5)
for measurement in measurements.features:
    props = measurement.properties
    print(f"Date: {props.observation_datetime}")  # Parsed datetime object
    print(f"Level: {props.water_level_masl_m} m above sea level")

API Reference

SGUClient

The main client class providing access to all SGU APIs.

• levels.observed - Observed groundwater level measurements
• levels.modeled - Modeled groundwater levels from SGU-HYPE
• chemistry - Groundwater chemistry sampling sites and analysis results

Configuration

from sgu_client import SGUConfig

config = SGUConfig(
    timeout=30,        # Request timeout in seconds
    max_retries=3,     # Maximum retry attempts
    debug=False        # Enable debug logging
)

Development

We recommend using uv for development:

# Clone the repository
git clone https://github.com/officialankan/sgu-client.git
cd sgu-client

# Install dependencies and sync environment
uv sync --all-extras

# Run tests
uv run pytest

# Format and lint code
uv run ruff format
uv run ruff check --fix

Release Process

To release a new version:

1. Create PR with your changes + version bump in pyproject.toml
2. PR will be tested and published to TestPyPI for verification
3. Once merged, the new version is automatically published to PyPI

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

Roadmap

• Initial release with observed and modeled groundwater levels
• Add example notebooks and tutorials
• Add support for groundwater chemistry API
• Add support for geological data API

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Sveriges geologiska undersökning (SGU) for providing open access to groundwater data
• Built with uv, Pydantic, and requests