Python-Blosc2
A Python wrapper for the extremely fast Blosc2 compression library
- Author:
The Blosc development team
- Contact:
- Github:
- Actions:
- PyPi:
- NumFOCUS:
- Code of Conduct:
What it is
C-Blosc2 is the new major version of C-Blosc, and is backward compatible with both the C-Blosc1 API and its in-memory format. Python-Blosc2 is a Python package that wraps C-Blosc2, the newest version of the Blosc compressor.
Currently Python-Blosc2 already reproduces the API of Python-Blosc, so it can be used as a drop-in replacement. However, there are a few exceptions for a full compatibility.
In addition, Python-Blosc2 aims to leverage the full C-Blosc2 functionality to support super-chunks (SChunk), multi-dimensional arrays (NDArray), metadata, serialization and other bells and whistles introduced in C-Blosc2.
Note: Python-Blosc2 is meant to be backward compatible with Python-Blosc data. That means that it can read data generated with Python-Blosc, but the opposite is not true (i.e. there is no forward compatibility).
SChunk: a 64-bit compressed store
A SChunk is a simple data container that handles setting, expanding and getting data and metadata. Contrarily to chunks, a super-chunk can update and resize the data that it contains, supports user metadata, and it does not have the 2 GB storage limitation.
Additionally, you can convert a SChunk into a contiguous, serialized buffer (aka cframe) and vice-versa; as a bonus, the serialization/deserialization process also works with NumPy arrays and PyTorch/TensorFlow tensors at a blazing speed:
while reaching excellent compression ratios:
Also, if you are a Mac M1/M2 owner, make you a favor and use its native arm64 arch (yes, we are distributing Mac arm64 wheels too; you are welcome ;-):
Read more about SChunk features in our blog entry at: https://www.blosc.org/posts/python-blosc2-improvements
NDArray: an N-Dimensional store
One of the latest and more exciting additions in Python-Blosc2 is the NDArray object. It can write and read n-dimensional datasets in an extremely efficient way thanks to a n-dim 2-level partitioning, allowing to slice and dice arbitrary large and compressed data in a more fine-grained way:
To wet you appetite, here it is how the NDArray object performs on getting slices orthogonal to the different axis of a 4-dim dataset:
We have blogged about this: https://www.blosc.org/posts/blosc2-ndim-intro
We also have a ~2 min explanatory video on why slicing in a pineapple-style (aka double partition) is useful:
Installing
Blosc is now offering Python wheels for the main OS (Win, Mac and Linux) and platforms. You can install binary packages from PyPi using pip:
pip install blosc2Documentation
The documentation is here:
https://blosc.org/python-blosc2/python-blosc2.html
Also, some examples are available on:
Building from sources
python-blosc2 comes with the C-Blosc2 sources with it and can be built in-place:
git clone https://github.com/Blosc/python-blosc2/
cd python-blosc2
git submodule update --init --recursive
python -m pip install -r requirements-build.txt
python setup.py build_ext --inplaceThat’s all. You can proceed with testing section now.
Testing
After compiling, you can quickly check that the package is sane by running the tests:
python -m pip install -r requirements-tests.txt
python -m pytest (add -v for verbose mode)Benchmarking
If curious, you may want to run a small benchmark that compares a plain NumPy array copy against compression through different compressors in your Blosc build:
PYTHONPATH=. python bench/pack_compress.pyLicense
The software is licenses under a 3-Clause BSD license. A copy of the python-blosc2 license can be found in LICENSE.txt.
Mailing list
Discussion about this module is welcome in the Blosc list:
Please follow @Blosc2 to get informed about the latest developments.
Citing Blosc
You can cite our work on the different libraries under the Blosc umbrella as:
@ONLINE{blosc,
author = {{Blosc Development Team}},
title = "{A fast, compressed and persistent data store library}",
year = {2009-2023},
note = {https://blosc.org}
}Enjoy!
Release files for blosc2 2.5.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| blosc2-2.5.1.tar.gz | 4.7 MB | Details |
Built distributions (wheels)
Total release size: 105.5 MB
Release files / blosc2-2.5.1.tar.gz
| Download URL | blosc2-2.5.1.tar.gz |
|---|---|
| Size | 4.7 MB |
| Tags | Source |
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Release files / blosc2-2.5.1-cp312-cp312-win_amd64.whl
| Download URL | blosc2-2.5.1-cp312-cp312-win_amd64.whl |
|---|---|
| Size | 2.3 MB |
| Tags | CPython 3.12 Windows x86-64 |
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Release files / blosc2-2.5.1-cp312-cp312-win32.whl
| Download URL | blosc2-2.5.1-cp312-cp312-win32.whl |
|---|---|
| Size | 1.9 MB |
| Tags | CPython 3.12 Windows x86-32 |
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Release files / blosc2-2.5.1-cp312-cp312-musllinux_1_1_x86_64.whl
| Download URL | blosc2-2.5.1-cp312-cp312-musllinux_1_1_x86_64.whl |
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| Size | 4.4 MB |
| Tags | CPython 3.12 Linux musl 1.1+ x86-64 |
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Release files / blosc2-2.5.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
| Download URL | blosc2-2.5.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl |
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| Tags | CPython 3.12 Linux glibc 2.17+ x86-64 |
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Release files / blosc2-2.5.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
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Release files / blosc2-2.5.1-cp312-cp312-macosx_11_0_arm64.whl
| Download URL | blosc2-2.5.1-cp312-cp312-macosx_11_0_arm64.whl |
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Release files / blosc2-2.5.1-cp312-cp312-macosx_10_9_x86_64.whl
| Download URL | blosc2-2.5.1-cp312-cp312-macosx_10_9_x86_64.whl |
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Release files / blosc2-2.5.1-cp311-cp311-win_amd64.whl
| Download URL | blosc2-2.5.1-cp311-cp311-win_amd64.whl |
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Release files / blosc2-2.5.1-cp311-cp311-win32.whl
| Download URL | blosc2-2.5.1-cp311-cp311-win32.whl |
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Release files / blosc2-2.5.1-cp311-cp311-musllinux_1_1_x86_64.whl
| Download URL | blosc2-2.5.1-cp311-cp311-musllinux_1_1_x86_64.whl |
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| Tags | CPython 3.11 Linux musl 1.1+ x86-64 |
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Release files / blosc2-2.5.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
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Release files / blosc2-2.5.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
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Release files / blosc2-2.5.1-cp311-cp311-macosx_11_0_arm64.whl
| Download URL | blosc2-2.5.1-cp311-cp311-macosx_11_0_arm64.whl |
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Release files / blosc2-2.5.1-cp311-cp311-macosx_10_9_x86_64.whl
| Download URL | blosc2-2.5.1-cp311-cp311-macosx_10_9_x86_64.whl |
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Release files / blosc2-2.5.1-cp310-cp310-win_amd64.whl
| Download URL | blosc2-2.5.1-cp310-cp310-win_amd64.whl |
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Release files / blosc2-2.5.1-cp310-cp310-win32.whl
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Release files / blosc2-2.5.1-cp39-cp39-musllinux_1_1_x86_64.whl
| Download URL | blosc2-2.5.1-cp39-cp39-musllinux_1_1_x86_64.whl |
|---|---|
| Size | 4.5 MB |
| Tags | CPython 3.9 Linux musl 1.1+ x86-64 |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.11.7
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Release files / blosc2-2.5.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
| Download URL | blosc2-2.5.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl |
|---|---|
| Size | 4.4 MB |
| Tags | CPython 3.9 Linux glibc 2.17+ x86-64 |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.11.7
|
Release files / blosc2-2.5.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
| Download URL | blosc2-2.5.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl |
|---|---|
| Size | 4.2 MB |
| Tags | CPython 3.9 Linux glibc 2.17+ ARM64 |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.11.7
|
Release files / blosc2-2.5.1-cp39-cp39-macosx_11_0_arm64.whl
| Download URL | blosc2-2.5.1-cp39-cp39-macosx_11_0_arm64.whl |
|---|---|
| Size | 3.5 MB |
| Tags | CPython 3.9 macOS 11.0+ ARM64 |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.11.7
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Release files / blosc2-2.5.1-cp39-cp39-macosx_10_9_x86_64.whl
| Download URL | blosc2-2.5.1-cp39-cp39-macosx_10_9_x86_64.whl |
|---|---|
| Size | 4.4 MB |
| Tags | CPython 3.9 macOS 10.9+ x86-64 |
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SHA-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.11.7
|
