[Numpy-discussion] next NumPy Newcomers' Hour - December 14th, 2023 at 10pm UTC

[Inessa Pawson]

Our next Newcomers' Hour will be held this Thursday, December 14th at 10pm
UTC. Stop by to ask questions, share your progress, celebrate success, or
just to say hi.

To add to the meeting agenda the topics you’d like to discuss, follow the
link: https://hackmd.io/3f3otyyuTte3FU9y3QzsLg?both.

Join the meeting via Zoom:
https://us06web.zoom.us/j/82563808729?pwd=ZFU3Z2dMcXBGb05YemRsaGE1OW5nQT09.

-- 
Cheers,
Inessa

Inessa Pawson
GitHub: inessapawson
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[Numpy-discussion] ANN: numexpr 2.8.8 released

[Francesc Alted]

Announcing NumExpr 2.8.8


Hi everyone,

NumExpr 2.8.8 is a release to deal mainly with issues appearing with
upcoming `NumPy` 2.0.  Also, some small fixes (support for simple complex
expressions like `ne.evaluate('1.5j')`) and improvements are included.

Project documentation is available at:

http://numexpr.readthedocs.io/

Changes from 2.8.7 to 2.8.8
---

* Fix re_evaluate not taking global_dict as argument. Thanks to Teng Liu
  (@27rabbitlt).

* Fix parsing of simple complex numbers.  Now, `ne.evaluate('1.5j')` works.
  Thanks to Teng Liu (@27rabbitlt).

* Fixes for upcoming NumPy 2.0:

  * Replace npy_cdouble with C++ complex. Thanks to Teng Liu (@27rabbitlt).
  * Add NE_MAXARGS for future numpy change NPY_MAXARGS. Now it is set to 64
to match NumPy 2.0 value. Thanks to Teng Liu (@27rabbitlt).

What's Numexpr?
---

Numexpr is a fast numerical expression evaluator for NumPy.  With it,
expressions that operate on arrays (like "3*a+4*b") are accelerated
and use less memory than doing the same calculation in Python.

It has multi-threaded capabilities, as well as support for Intel's
MKL (Math Kernel Library), which allows an extremely fast evaluation
of transcendental functions (sin, cos, tan, exp, log...) while
squeezing the last drop of performance out of your multi-core
processors.  Look here for a some benchmarks of numexpr using MKL:

https://github.com/pydata/numexpr/wiki/NumexprMKL

Its only dependency is NumPy (MKL is optional), so it works well as an
easy-to-deploy, easy-to-use, computational engine for projects that
don't want to adopt other solutions requiring more heavy dependencies.

Where I can find Numexpr?
-

The project is hosted at GitHub in:

https://github.com/pydata/numexpr

You can get the packages from PyPI as well (but not for RC releases):

http://pypi.python.org/pypi/numexpr

Documentation is hosted at:

http://numexpr.readthedocs.io/en/latest/

Share your experience
-

Let us know of any bugs, suggestions, gripes, kudos, etc. you may
have.

Enjoy data!

-- 
Francesc Alted
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[Numpy-discussion] Re: Numpy not working

[Friedrich Romstedt]

Hi Christophe,

Am So., 10. Dez. 2023 um 19:49 Uhr schrieb Christophe Nassar
:
>
> "(tf) C:\*\\\DiffMorph-master\DiffMorph-master>morph.py -s 
> images/img_1.jpg -t images/img_2.jpg
> Traceback (most recent call last):
>   File "C:\Users\chris\Desktop\DiffMorph-master\DiffMorph-master\morph.py", 
> line 3, in 
> import numpy as np
> ModuleNotFoundError: No module named 'numpy'"

It looks to me like if the Python interpreter used to run your script
'morph.py' wenn you just say '> morph.py [...]' is different from the
one you're using when e.g. running pip.

Maybe you can try '> python morph.py [...]' and see if this works, or
just '> python' followed by '>>> import numpy' to narrow down the
error.

You might also try to use the 'py' wrapper, it's got several options
to specify which of the installed Pythons to run.

HTH
Friedrich
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