From Newsgroup: comp.lang.python.announce

Announcing Python-Blosc2 3.6.1
==============================

In this release:

✅ Blosc2 now suppports fancy indexing (and orthogonal indexing)
✅ Added fast path for 1D fancy indexing
✅ More complex slicing is now supported for lazy expressions
✅ Blosc2 indexing more consistent with NumPy
✅ Comprehensive ``squeeze`` function which squeezes only specified
dimensions
✅ Correctly point to most recent C-blosc2 version 2.19.1

We have blogged about the new fancy indexing support: https://www.blosc.org/posts/blosc2-fancy-indexing/

You can think of Python-Blosc2 3.x as an extension of NumPy/numexpr that:

- Can deal with ndarrays compressed using first-class codecs & filters.
- Performs many kind of math expressions, including reductions, indexing...
- Supports NumPy ufunc mechanism: mix and match NumPy and Blosc2
computations.
- Integrates with Numba and Cython via UDFs (User Defined Functions).
- Adheres to modern NumPy casting rules way better than numexpr.
- Performs linear algebra operations (like ``blosc2.matmul()``).

Install it with::

pip install blosc2 --update # 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
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