dclimate-client-py 0.1.2

Python client library for accessing dClimate weather and climate data

dClimate-Client-Py

Retrieve dClimate GIS zarr datasets stored on IPFS

Uses py-hamt to access Zarr data structures stored efficiently on IPFS.

Filtering and aggregation are packaged into convenience functions optimized for flexibility and performance.

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Usage

from datetime import datetime
import dclimate_client_py as client
from dclimate_client_py import dClimateClient

# --- Recommended: Using dClimateClient (async context manager) ---

async def main():
    # The client manages IPFS connections automatically
    # No need to import or configure KuboCAS directly!
    async with dClimateClient() as dclimate:
        # Load datasets by name from the internal catalog
        # For datasets with multiple variants, you must specify which variant
        dataset = await dclimate.load_dataset(
            dataset="2m_temperature",
            collection="era5",
            variant="finalized",  # Required for multi-variant datasets
            return_xarray=False   # Returns GeotemporalData wrapper (default)
        )

        # Apply queries using the GeotemporalData interface
        dataset_filtered = dataset.point(latitude=40.875, longitude=-104.875)
        dataset_filtered = dataset_filtered.time_range(
            datetime(2023, 1, 1),
            datetime(2023, 1, 5)
        )
        data_dict = dataset_filtered.as_dict()
        print(data_dict['data'])

# Custom IPFS endpoints (optional)
async def main_custom_ipfs():
    async with dClimateClient(
        gateway_base_url="https://ipfs.io",
        rpc_base_url="http://localhost:5001"
    ) as dclimate:
        dataset = await dclimate.load_dataset(
            dataset="2m_temperature",
            collection="era5",
            variant="finalized"
        )
        # Query dataset...

# Get raw xarray.Dataset directly
async def main_xarray():
    async with dClimateClient() as dclimate:
        xr_dataset = await dclimate.load_dataset(
            dataset="2m_temperature",
            collection="era5",
            variant="finalized",
            return_xarray=True  # Returns xarray.Dataset
        )
        print(xr_dataset)

# List available datasets in the catalog (synchronous helper)
catalog = client.list_dataset_catalog()
for collection in catalog:
    print(f"Collection: {collection['collection']}")
    for dataset in collection['datasets']:
        print(f"  Dataset: {dataset['dataset']}")
        for variant in dataset['variants']:
            print(f"    Variant: {variant['variant']}")

More examples can be found at dClimate Jupyter Notebooks. To run your own IPFS gateway follow the instructions for installing ipfs. For additional assistance find us on Discord, if you are an organization or business reach out to us at community at dclimate dot net.

Create and activate a virtual environment:

uv venv .venv
source .venv/bin/activate  # macOS/Linux
.\.venv\Scripts\activate   # Windows

Install Dependencies

uv sync --extra dev --extra testing

Run tests for your local environment

uv run pytest tests/

Use Coverage

uv run pytest --cov=dclimate_client_py tests/ --cov-report=xml

Environment requirements

• Optionally you can run your own IPFS Server to host your own datasets or connect to others.

File breakdown:

client.py

Entrypoint to code, contains geo_temporal_query, which combines all possible subsetting and aggregation logic in a single function. Can output the data as either a dict or bytes representing an xarray dataset.

---

dclimate_zarr_errors.py

Various exceptions to be raised for bad or invalid user input.

---

geo_utils.py

Functions to manipulate xarray datasets. Contains polygon, rectangle, circle and point spatial subsetting options, as well as temporal subsetting. Also allows for both spatial and temporal aggregations.

---

ipfs_retrieval.py

Functions for resolving IPNS names, traversing the dClimate STAC catalog stored on IPFS, and loading Zarr datasets using py-hamt. Handles interaction with IPFS gateways and RPC endpoints.