xarray-prism 2603.0.0

A multi-format and multi-storage xarray engine with automatic engine detection, and ability to register new data format and uri type for climate data.

Xarray Prism Engine

A multi-format and multi-storage xarray engine with automatic engine detection, and ability to register new data format and uri type for climate data.

[!Important] If you encounter with a data formats that prism engine is not able to open, please files an issue report here. This helps us to improve the engine enabling users work with different kinds of climate data.

Installation

Install via PyPI

pip install xarray-prism

Install via Conda

conda install xarray-prism

Quick Start

Using with xarray

import xarray as xr

# Auto-detect format
ds = xr.open_dataset("my_data.unknown_fmt", engine="prism")

# Remote Zarr on S3
ds = xr.open_dataset(
    "s3://freva/workshop/tas.zarr",
    engine="prism",
    storage_options={
        "anon": True,
        "client_kwargs": {
            "endpoint_url": "https://s3.eu-dkrz-1.dkrz.cloud"
        }
    }
)

# Remote NetCDF3 on S3
ds = xr.open_dataset(
    "s3://freva/workshop/tas.nc",
    engine="prism",
    storage_options={
        "anon": True,
        "client_kwargs": {
            "endpoint_url": "https://s3.eu-dkrz-1.dkrz.cloud"
        }
    }
)

# Remote NetCDF4 on S3
ds = xr.open_dataset(
    "s3://freva/workshop/tas.nc4",
    engine="prism",
    storage_options={
        "anon": True,
        "client_kwargs": {
            "endpoint_url": "https://s3.eu-dkrz-1.dkrz.cloud"
        }
    }
)

# Remote Zarr on S3 - non-anon
ds = xr.open_dataset(
    "s3://bucket/data.zarr",
    engine="prism",
    storage_options={
        "key": "YOUR_KEY",
        "secret": "YOUR_SECRET",
        "client_kwargs": {
            "endpoint_url": "S3_ENDPOINT"
        }
    }
)

# OPeNDAP from THREDDS
ds = xr.open_dataset(
    "https://icdc.cen.uni-hamburg.de/thredds/dodsC/ftpthredds/ar5_sea_level_rise/gia_mean.nc",
    engine="prism"
)

# Local GRIB file
ds = xr.open_dataset("forecast.grib2", engine="prism")

# GeoTIFF
ds = xr.open_dataset("satellite.tif", engine="prism")

# tip: Handle the cache manually by yourself
xr.open_dataset(
    "simplecache::s3://bucket/file.nc3",
    engine="prism",
    storage_options={
        "s3": {"anon": True, "client_kwargs": {"endpoint_url": "..."}},
        "simplecache": {"cache_storage": "/path/to/cache"}
    }
)

# Even for the tif format on the S3 you can pass the credential through
# storage_options which is not supported by rasterio:
xr.open_dataset(
    "s3://bucket/file.tif",
    engine="prism",
    storage_options={
        "key": "YOUR_KEY",
        "secret": "YOUR_SECRET",
        "client_kwargs": {
            "endpoint_url": "S3_ENDPOINT"
        }
    }
)

Supported Formats

Data format	Remote backend	Local FS	Cache
GRIB	cfgrib + fsspec	cfgrib	fsspec simplecache (full-file)
Zarr	zarr + fsspec	zarr	chunked key/value store
NetCDF3	scipy + fsspec	scipy	fsspec byte cache (5 MB blocks but full dowload)
NetCDF4/HDF5	h5netcdf + fsspec	h5netcdf	fsspec byte cache (5 MB block)
GeoTIFF	rasterio + fsspec	rasterio	GDAL/rasterio block cache (5 MB block)
OPeNDAP/DODS	netCDF4	n/a	n/a

[!WARNING] Remote GRIB & NetCDF3 require full file download

Unlike Zarr or HDF5, these formats don't support partial/chunk reads over the network.

By default, xarray-prism caches files in the system temp directory. This works well for most cases. If temp storage is a concern (e.g., limited space or cleared on reboot), you can specify a persistent cache:

Option	How
Environment variable	export XARRAY_PRISM_CACHE=/path/to/cache
Per-call	storage_options={"simplecache": {"cache_storage": "/path"}}
Default	System temp directory

Cache management

You can inspect or evict the cache manually:

import xarray_prism as xp

xp.cache_info()
# {'files': 12, 'size_bytes': 2400000000, 'path': '/tmp/xarray-prism-cache'}

# Preview what would be removed
xp.clear_cache(dry_run=True)

# Evict with custom thresholds
xp.clear_cache(max_age_days=3, max_size_gb=2)

# Remove everything
xp.clear_cache(max_age_days=0, max_size_gb=0)

[!NOTE] max_age_days and max_size_gb can also be set via the following environment variables:

Policy	Default	Override
TTL (last-access)	7 days	XARRAY_PRISM_MAX_AGE_DAYS=N
Size cap (LRU)	10 GB	XARRAY_PRISM_MAX_SIZE_GB=N

Customization

Custom Format Detectors and URI Types

You can extend xarray-prism with custom format detectors, URI types, and open handlers by providing a small plugin package. Registration happens at import time, so importing the plugin activates it.

Plugin structure

xarray_prism_myplugin/
  __init__.py   # imports the plugin module (triggers registration)
  plugin.py     # detectors, URI types, and open handlers
pyproject.toml

Plugin implementation

xarray_prism_myplugin/__init__.py

from .plugin import *  # noqa: F401,F403

xarray_prism_myplugin/plugin.py

import xarray as xr
from xarray_prism import register_detector, register_uri_type, registry


@register_uri_type(priority=100)
def detect_myfs_uri(uri: str):
    """Detect a custom filesystem URI."""
    if uri.lower().startswith("myfs://"):
        return "myfs"
    return None


@register_detector(priority=100)
def detect_foo_format(uri: str):
    """Detect a custom file format."""
    if uri.lower().endswith(".foo"):
        return "foo"
    return None


@registry.register("foo", uri_type="myfs")
def open_foo_from_myfs(uri: str, **kwargs):
    """Open .foo files from myfs:// URIs."""
    translated = uri.replace("myfs://", "https://my-gateway.example/")
    return xr.open_dataset(translated, engine="h5netcdf", **kwargs)

Plugin installation

pyproject.toml

[project]
name = "xarray-prism-myplugin"
version = "0.1.0"
dependencies = ["xarray-prism"]

[project.entry-points."xarray_prism.plugins"]
myplugin = "xarray_prism_myplugin"

Using the plugin

After installing the plugin package, import it once to activate the registrations:

import xarray_prism_myplugin  # activates detectors and handlers

import xarray as xr
ds = xr.open_dataset("myfs://bucket/path/data.foo", engine="prism")

Development

Setup Development Environment

# Start test services (MinIO, THREDDS)
docker-compose -f dev-env/docker-compose.yaml up -d --remove-orphans

# Create conda environment
conda create -n xarray-prism python=3.12 -y
conda activate xarray-prism

# Install package in editable mode with dev dependencies
pip install -e ".[dev]"

Running Tests

# Run tests
tox -e test

# Run with coverage
tox -e test-cov

# Lint
tox -e lint

# Type checking
tox -e types

# Auto-format code
tox -e format

Creating a Release

Releases are managed via GitHub Actions and tox:

# Tag a new release (creates git tag)
tox -e release

The release workflow is triggered automatically when:

• A version tag (v*.*.*) is pushed -> Full release to PyPI
• Manual workflow dispatch with RC number -> Pre-release to PyPI