Announcing Python-Blosc2 3.0.0-rc1
==================================
The Blosc development team is pleased to announce the first release release of
Python-Blosc2 3.0.0. In this release, we have focused on the making of a
compute engine that can work with compressed data in a NumPy-like fashion.
You can think of Python-Blosc2 3.0 as a replacement of numexpr, but better :-)
As always, we would like to get feedback from the community before the final
release. We are providing binary wheels that you can easily install from PyPI
with:
pip install blosc2==3.0.0rc1
For more info, you can have a look at the release notes in:
https://github.com/Blosc/python-blosc2/releases
Docs and examples are available in the documentation site:
https://www.blosc.org/python-blosc2
Code example::
import blosc2
from time import time
# Create some data operands
N = 10_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 * N, shape=(N, N))
# Expression
t0 = time()
expr = ((a**3 + blosc2.sin(c * 2)) < b) & (c > 0)
print(f"Time to create expression: {time()-t0:.5f}")
# Evaluate and get a NDArray as result
t0 = time()
out = expr.compute()
print(f"Time to compute: {time()-t0:.5f}")
This will output something like::
Time to create expression: 0.00041
Time to compute: 0.56215
Note that the expression is computed lazily, only when the `compute`
method is called.
BTW, I'll be teaching about Python-Blosc2 3.0 in my forthcoming
tutorial at PyData Global 2024
(https://pydata.org/global2024/schedule). Be sure to be there for an
exciting introduction to the bells and whistles of the shiny new
computation engine and its enhanced capabilities to deal with large
datasets.
Thanks and see you there!
--
Francesc Alted
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