atlas-python

Thousands of NetCDF datasets in one immutable file. It sits on local disk or on object storage: S3, GCS, Azure, or HTTP. A Rust core, five operations, and one command.

pip install atlas-python

atlas create /data/nc /data/collection
atlas ls     /data/collection
atlas show   /data/collection 2024-01.nc
atlas info   /data/collection
atlas rm     /data/collection 2024-02.nc 2024-03.nc

python -m atlas runs the same command without a PATH lookup, for a shell that cannot find atlas.

Extra Install Adds
cloud pip install "atlas-python[cloud]" S3 / GCS / Azure / HTTP via obstore

numpy, xarray, and dask install automatically.

Five operations

The same five as a library:

import atlas

atlas.create("/data/nc", "/data/collection")   # from a directory of NetCDF files
atlas.list_datasets("/data/collection")        # ['2024-01.nc', '2024-02.nc', '2024-03.nc']
atlas.describe("/data/collection", "2024-01.nc")  # types, shapes, attrs, statistics
atlas.info("/data/collection")                 # counts, size, codec, statistics
atlas.remove("/data/collection", ["2024-02.nc"])  # updates the mask

Every one takes a local path, a URL, or an obstore handle:

atlas ls s3://my-bucket/collections/2024 --region eu-west-1

atlas.list_datasets("s3://my-bucket/collections/2024", region="eu-west-1")

Two things to internalise

One write builds a collection. There is no append, no in-place update, and no flush. The file has a valid trailer, or it is no collection. To change a dataset, rebuild the collection. remove is the one exception. It writes a small mask file, and never touches the container. It therefore reclaims no space, and moves no ordinal.

Python writes. Rust reads array data. From Python you build a collection and read its metadata. That is the dataset names, the array types, the shapes, the chunk shapes, the fill values, the attributes, and the statistics of the write. There is no read_array. Array values come from the Rust API.

That split makes the read side free. The footer an open already fetched answers every metadata call. A catalogue of a thousand datasets is therefore one request.

What show gives you

$ atlas show /data/collection 2024-01.nc
dataset 2024-01.nc {
dimensions:
	lat = 4 ;
	lon = 6 ;
variables:
	float32 temperature(lat, lon) ;
		temperature:_FillValue = nan ;
		temperature:units = "celsius" ;
		// stats: count=24  min=1.0  max=24.0
	string station(lat) ;
		// stats: count=4  min="a"  max="d"

// global attributes:
		:month = 1 ;

// ordinal 0, segment bytes 8..1691
}

The shape follows ncdump -h. It adds the statistics of the write: the minimum, the maximum, and how many elements are missing. --json on any read command gives the same content as a structure.

Ingest

create scans a directory for .nc, .nc4, .cdf, and .netcdf, and descends into every subdirectory. It sorts them, and writes one dataset per file, named after the file. Each coordinate and data variable becomes an array. Each variable attribute becomes a per-array attribute. _FillValue becomes the fill of the array.

Each file opens with dask chunking. A file far larger than memory therefore streams block by block. --chunk-size sets the block budget, and defaults to 128 MiB. It is about the memory ceiling per variable. Those blocks also become the stored chunk shape.

Nothing at the destination is readable until every file lands. A failure part-way leaves no collection, and not a partial one. on_error="skip", or --skip-errors, trades that for progress.

Atlas cannot store every numpy dtype, and bool is the common case. One such variable fails the whole file by default. on_unsupported="skip", or --skip-unsupported, leaves out that one array and lands the rest.

--log-file PATH, or atlas.log_to_file(path), appends every error and warning to a file, each with its reason and the file it came from.

dtypes

numpy atlas
int / uint widths, float32, float64 the same
datetime64[ns] timestamp_nanoseconds
timedelta64[*] int64 nanoseconds, plus a unit marker
object / S / U string

bool, binary, and the list types work as an attribute value. No one of them works yet as an array element type. timestamp_nanoseconds goes the other way: it is an array element type, and no attribute takes it. Store the nanoseconds as int64 and name the unit beside it.

Documentation

The full docs sit at https://maris-development.github.io/atlas/. They hold the command reference, and guides for creating, inspecting, removing, dtypes, reading data, and cloud storage.

The format itself is documented in docs/.

License

Apache-2.0. See LICENSE.

Project links

Data verified by PyPI on Sep 8, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.17.2)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for robinkooyman from gravatar.com robinkooyman

Credits

Author: Robin Kooyman

GitHub Statistics

Data verified by PyPI on Sep 8, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

Apache-2.0
View SPDX License List

Requires

Python >=3.10

Provides Extra

cloud test

Tags

array ndarray xarray scientific storage object-store

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

atlas_python-0.17.1.tar.gz (209.6 kB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

atlas_python-0.17.1-cp310-abi3-win_amd64.whl (4.8 MB view details)

Uploaded CPython 3.10+Windows x86-64

atlas_python-0.17.1-cp310-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.6 MB view details)

Uploaded CPython 3.10+manylinux: glibc 2.17+ x86-64

atlas_python-0.17.1-cp310-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (5.8 MB view details)

Uploaded CPython 3.10+manylinux: glibc 2.17+ ARM64

atlas_python-0.17.1-cp310-abi3-macosx_11_0_arm64.whl (5.0 MB view details)

Uploaded CPython 3.10+macOS 11.0+ ARM64

atlas_python-0.17.1-cp310-abi3-macosx_10_12_x86_64.whl (5.2 MB view details)

Uploaded CPython 3.10+macOS 10.12+ x86-64

File details

Details for the file atlas_python-0.17.1.tar.gz.

File metadata

  • Download URL: atlas_python-0.17.1.tar.gz
  • Upload date:
  • Size: 209.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for atlas_python-0.17.1.tar.gz
Algorithm Hash digest
SHA256 5e06bed96609aebecc96a79c10b79d9acdb8f4b81180a5c81a1600efeaa70b97
MD5 4855da2d3eea37a171cb3ce5cae4a1a8
BLAKE2b-256 7e8f62add1524a53afb47b6c6f785e62a40745cfa29af27e0e00590867eda723

See more details on using hashes here.

Provenance

The following attestation bundles were made for atlas_python-0.17.1.tar.gz:

Publisher: atlas-python-release.yaml on maris-development/atlas

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file atlas_python-0.17.1-cp310-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for atlas_python-0.17.1-cp310-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 f4acd2861713d41cabdaf972ee2b204baffa4cec969f029e1499069ca1244d10
MD5 bdac36a8f5fb7d182d4bdf4009f805a3
BLAKE2b-256 0a1a67a0f05a26184588cdd5840081a6d14be796499ce79dcf0657f8f360605b

See more details on using hashes here.

Provenance

The following attestation bundles were made for atlas_python-0.17.1-cp310-abi3-win_amd64.whl:

Publisher: atlas-python-release.yaml on maris-development/atlas

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file atlas_python-0.17.1-cp310-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for atlas_python-0.17.1-cp310-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 64b18328d56bf43952ab98ad388cea291272e629ef5b4c398d2b40670c228706
MD5 34c8444f8911a854009da0c8ab75bd9d
BLAKE2b-256 293a368141fda9002a4a0c184751868ec75a533030cf7abc635f666809e65543

See more details on using hashes here.

Provenance

The following attestation bundles were made for atlas_python-0.17.1-cp310-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl:

Publisher: atlas-python-release.yaml on maris-development/atlas

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file atlas_python-0.17.1-cp310-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for atlas_python-0.17.1-cp310-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 962e01656dcfa324599bb7fb20b3794ca0a3353d9b3cd0ac2ba7044f1d850378
MD5 4ae4c445cd494722ee35dd6dc7a74b37
BLAKE2b-256 31cd64cb89c37593f15091c7f4d76ea0671055e971fbfafca7ee9de7975a3a89

See more details on using hashes here.

Provenance

The following attestation bundles were made for atlas_python-0.17.1-cp310-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl:

Publisher: atlas-python-release.yaml on maris-development/atlas

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file atlas_python-0.17.1-cp310-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for atlas_python-0.17.1-cp310-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 2d5e48877b22868713fe35ad5ef57b5c6deadcb2f6e13de3d828bb9bf5b5cee6
MD5 0bec0409a323bd92cc84a214f769bec9
BLAKE2b-256 ec07324aaf7cecb03441579a04f76d9ff187c0f907f6b12f3f5495d6c2829d13

See more details on using hashes here.

Provenance

The following attestation bundles were made for atlas_python-0.17.1-cp310-abi3-macosx_11_0_arm64.whl:

Publisher: atlas-python-release.yaml on maris-development/atlas

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file atlas_python-0.17.1-cp310-abi3-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for atlas_python-0.17.1-cp310-abi3-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 b1daec98fadb26d900049844ed3d7f36387586fcaea92ddfa37692cf392fff5b
MD5 71fddbbca8cdad4689ba543674721b17
BLAKE2b-256 0bcbb4fb07a98f1f7bd204e9115a77bd47776a6911919a682835394f2e7bcd08

See more details on using hashes here.

Provenance

The following attestation bundles were made for atlas_python-0.17.1-cp310-abi3-macosx_10_12_x86_64.whl:

Publisher: atlas-python-release.yaml on maris-development/atlas

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

0.17.2

6 files

This release

0.17.1 This release

6 files

0.17.0

6 files

0.16.4

6 files

0.16.3

6 files

0.16.2

6 files

0.16.1

6 files

0.16.0

6 files

0.14.0

6 files

0.13.1

6 files

0.13.0

6 files

0.12.0

6 files

0.10.0

6 files

0.9.1

6 files

0.9.0

6 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page