omfiles 1.1.0

Python bindings for the Open-Meteo file format

Python Bindings for Open Meteo File Format

Features

• Read Open-Meteo (.om) files directly from cloud storage using Python
• Traverse the hierarchical data structure
• Arrays/array slices are returned directly as NumPy arrays
• Support for fsspec and xarray
• Chunked data access behind the scenes

Installation

pip install omfiles

Pre-Built Wheels & Platform Support

We provide pre-built wheels for the following platforms:

• Linux x86_64 (manylinux_2_28_x86_64)
• Linux aarch64 (manylinux_2_28_aarch64)
• Linux musl x86_64 (musllinux_1_2_x86_64)
• Windows x86_64 (win_amd64)
• Windows ARM64 (win_arm64)
• macOS x86_64 (macosx_10_12_x86_64)
• macOS ARM64 (macosx_11_0_arm64)

Stability Notice

This project is now stable as of version 1.0.0. The public API is considered stable and will follow semantic versioning. Breaking changes will not be introduced in 1.x releases without a major version bump.

Reading

Reading Files without Hierarchy

OM files are structured like a tree of variables. The following example assumes that the file test_file.om contains an array variable as a root variable which has a dimensionality greater than 2 and a size of at least 2x100:

from omfiles import OmFileReader

reader = OmFileReader("test_file.om")
data = reader[0:2, 0:100, ...]
reader.close() # Close the reader to release resources

Reading Hierarchical Files, e.g. S3 Spatial Files

import fsspec
import numpy as np
from omfiles import OmFileReader

# Example: URI for a spatial data file in the `data_spatial` S3 bucket
# See data organization details: https://github.com/open-meteo/open-data?tab=readme-ov-file#data-organization
# Note: Spatial data is only retained for 7 days. The example file below may no longer exist.
# Please update the URI to match a currently available file.
s3_uri = "s3://openmeteo/data_spatial/dwd_icon/2025/09/23/0000Z/2025-09-30T0000.om"

# Create and open filesystem, wrapping it in a blockcache
backend = fsspec.open(
    f"blockcache::{s3_uri}",
    mode="rb",
    s3={"anon": True, "default_block_size": 65536},  # s3 settings
    blockcache={"cache_storage": "cache"},  # blockcache settings
)
# Create reader from the fsspec file object using a context manager.
# This will automatically close the file when the block is exited.
with OmFileReader(backend) as root:
    # We are at the root of the data hierarchy!
    # What type of node is this?
    print(f"root.is_array: {root.is_array}")  # False
    print(f"root.is_scalar: {root.is_scalar}")  # False
    print(f"root.is_group: {root.is_group}")  # True

    temperature_reader = root.get_child_by_name("temperature_2m")
    print(f"temperature_reader.is_array: {temperature_reader.is_array}")  # True
    print(f"temperature_reader.is_scalar: {temperature_reader.is_scalar}")  # False
    print(f"temperature_reader.is_group: {temperature_reader.is_group}")  # False

    # What shape does the stored array have?
    print(f"temperature_reader.shape: {temperature_reader.shape}")  # (1441, 2879)

    # Read all data from the array
    temperature_data = temperature_reader.read_array((...))
    print(f"temperature_data.shape: {temperature_data.shape}")  # (1441, 2879)

    # It's also possible to read any subset of the array
    temperature_data_subset1 = temperature_reader.read_array((slice(0, 10), slice(0, 10)))
    print(temperature_data_subset1)
    print(f"temperature_data_subset1.shape: {temperature_data_subset1.shape}")  # (10, 10)

    # Numpy basic indexing is supported for direct access if the reader is an array.
    temperature_data_subset2 = temperature_reader[0:10, 0:10]
    print(temperature_data_subset2)
    print(f"temperature_data_subset2.shape: {temperature_data_subset2.shape}")  # (10, 10)

    # Compare the two temperature subsets and verify that they are the same
    are_equal = np.array_equal(temperature_data_subset1, temperature_data_subset2, equal_nan=True)
    print(f"Are the two temperature subsets equal? {are_equal}")

Writing

Single Array

import numpy as np
from omfiles import OmFileWriter

# Create sample data
data = np.random.rand(100, 100).astype(np.float32)

# Initialize writer
writer = OmFileWriter("simple.om")

# Write array with compression
array_variable = writer.write_array(
    data,
    chunks=[50, 50],
    scale_factor=1.0,
    add_offset=0.0,
    compression="pfor_delta_2d",
    name="data"
)

# Finalize the file using array_variable as entry-point
writer.close(array_variable)

Hierarchical Structure

import numpy as np
from omfiles import OmFileWriter

# Create sample data
features = np.random.rand(1000, 64).astype(np.float32)
labels = np.random.randint(0, 10, size=(1000,), dtype=np.int32)

# Initialize writer
writer = OmFileWriter("hierarchical.om")

# Write child arrays first
features_var = writer.write_array(features, chunks=[100, 64], name="features", compression="pfor_delta_2d")
labels_var = writer.write_array(labels, chunks=[100], name="labels")
metadata_var = writer.write_scalar(42, name="metadata")

# Create root group with children
root_var = writer.write_group(
    name="root",
    children=[features_var, labels_var, metadata_var],
)
# Finalize the file using root_var as entry-point into the hierarchy
writer.close(root_var)

Development

# install the required dependencies in .venv directory
uv sync
# to run the tests
uv run pytest tests/
# to build the wheels
uv run build
# or to trigger maturin directly:
# maturin develop

Tests

cargo test

runs rust tests.

uv run pytest tests/

runs Python tests.

Python Type Stubs

Can be generated from the rust doc comments via

cargo run stub_gen