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Announcing Python-Blosc2 4.0.0

This is patch release which updates the miniexpr version to fix a bug for ubuntu ARM64 failure.

You can think of Python-Blosc2 4.x as an extension of NumPy/numexpr that:

  • Can deal with NDArray compressed objects using first-class codecs & filters.
  • Performs many kinds of math expressions, including reductions, indexing...
  • Supports multi-threading and SIMD acceleration (via numexpr/miniexpr).
  • Can operate with data from other libraries (like PyTables, h5py, Zarr, Dask, etc).
  • Supports NumPy ufunc mechanism: mix and match NumPy and Blosc2 computations.
  • Integrates with Numba and Cython via UDFs (User Defined Functions).
  • Adheres to modern array API standard conventions (https://data-apis.org/array-api/).
  • Can perform linear algebra operations (like blosc2.tensordot()).

Install it with:

pip install blosc2 --upgrade   # if you prefer wheels
conda install -c conda-forge python-blosc2 mkl  # if you prefer conda and MKL

For more info, you can have a look at the release notes in:

https://github.com/Blosc/python-blosc2/releases

Code example:

from time import time
import blosc2
import numpy as np

# Create some data operands
N = 20_000
a = blosc2.linspace(0, 1, N * N, dtype="float32", shape=(N, N))
b = blosc2.linspace(1, 2, N * N, shape=(N, N))
c = blosc2.linspace(-10, 10, N)  # broadcasting is supported

# Expression
t0 = time()
expr = ((a**3 + blosc2.sin(c * 2)) < b) & (c > 0)
print(f"Time to create expression: {time()-t0:.5f}")

# Evaluate while reducing (yep, reductions are in) along axis 1
t0 = time()
out = blosc2.sum(expr, axis=1)
t1 = time() - t0
print(f"Time to compute with Blosc2: {t1:.5f}")

# Evaluate using NumPy
na, nb, nc = a[:], b[:], c[:]
t0 = time()
nout = np.sum(((na**3 + np.sin(nc * 2)) < nb) & (nc > 0), axis=1)
t2 = time() - t0
print(f"Time to compute with NumPy: {t2:.5f}")
print(f"Speedup: {t2/t1:.2f}x")

assert np.all(out == nout)
print("All results are equal!")

This will output something like (using an Intel i9-13900K CPU here):

Time to create expression: 0.00033
Time to compute with Blosc2: 0.46387
Time to compute with NumPy: 2.57469
Speedup: 5.55x
All results are equal!

See a more in-depth example, explaining why Python-Blosc2 is so fast, at:

https://www.blosc.org/python-blosc2/getting_started/overview.html#operating-with-ndarrays

Sources repository

The sources and documentation are managed through GitHub services at:

https://github.com/Blosc/python-blosc2

Python-Blosc2 is distributed using the BSD license, see https://github.com/Blosc/python-blosc2/blob/main/LICENSE.txt for details.

Mastodon feed

Follow https://fosstodon.org/@Blosc2 to get informed about the latest developments.

Enjoy!

  • Blosc Development Team Compress Better, Compute Bigger