Mailman 3 March 2022 - NumPy-Discussion

ANN: SfePy 2022.1
by Robert Cimrman 30 Mar '22

30 Mar '22

I am pleased to announce the release of SfePy 2022.1. Description ----------- SfePy (simple finite elements in Python) is a software for solving systems of coupled partial differential equations by finite element methods. It is distributed under the new BSD license. Home page: https://sfepy.org Mailing list: https://mail.python.org/mm3/mailman3/lists/sfepy.python.org/ Git (source) repository, issue tracker: https://github.com/sfepy/sfepy Highlights of this release -------------------------- - new handling of state variables data and State class removal - many new sensitivity analysis terms based on multi-linear term implementation For full release notes see [1]. Cheers, Robert Cimrman [1] http://docs.sfepy.org/doc/release_notes.html#id1 --- Contributors to this release in alphabetical order: Robert Cimrman Robert T. McGibbon Vladimir Lukes

1 0

Google Season of Docs
by Melissa Mendonça 25 Mar '22

25 Mar '22

Hi all! I wrote drafts for both the NumPy and SciPy projects for Google Season of Docs. You'll notice they are very similar - the Google person responsible for the program suggested this may have a better chance of success instead of sending one proposal for the two projects. Here they are for review and feedback: NumPy: https://hackmd.io/@melissawm/r1yuKepbc SciPy: https://hackmd.io/@melissawm/HJqey27f9 The deadline for submission is March 25th. Feel free to add comments, corrections and suggestions. Thanks! - Melissa p.s. If you are interested in being a mentor (or co-mentor), please reach out so I can list you in the project.

1 1

np.bool_ vs Python bool behavior
by Jacob Reinhold 21 Mar '22

21 Mar '22

A pain point I ran into a while ago was assuming that an np.ndarray with dtype=np.bool_ would act similarly to the Python built-in boolean under addition. This is not the case, as shown in the following code snippet: >>> np.bool_(True) + True True >>> True + True 2 In fact, I'm somewhat confused about all the arithmetic operations on boolean arrays: >>> np.bool_(True) * True True >>> np.bool_(True) / True 1.0 >>> np.bool_(True) - True TypeError: numpy boolean subtract, the `-` operator, is not supported, use the bitwise_xor, the `^` operator, or the logical_xor function instead. >>> for x, y in ((False, False), (False, True), (True, False), (True, True)): print(np.bool_(x) ** y, end=" ") 1 0 1 1 I get that addition corresponds to "logical or" and multiplication corresponds to "logical and", but I'm lost on the division and exponentiation operations given that addition and multiplication don't promote the dtype to integers or floats. If arrays stubbornly refused to ever change type or interact with objects of a different type under addition, that'd be one thing, but they do change: >>> np.uint8(0) - 1 -1 >>> (np.uint8(0) - 1).dtype dtype('int64') >>> (np.uint8(0) + 0.1).dtype dtype('float64') This dtype change can also be seen in the division and exponentiation above for np.bool_. Why the discrepancy in behavior for np.bool_? And why are arithmetic operations for np.bool_ inconsistently promoted to other data types? If all arithmetic operations on np.bool_ resulted in integers, that would be consistent (so easier to work with) and wouldn't restrict expressiveness because there are also "logical or" (|) and "logical and" (&) operations available. Alternatively, division and exponentiation could throw errors like subtract, but the discrepancy between np.bool_ and the Python built-in bool for addition and multiplication would remain. For context, I ran into an issue with this discrepancy in behavior while working on an image segmentation problem. For binary segmentation problems, we make use of boolean arrays to represent where an object is (the locations in the array which are "True" correspond to the foreground/object-of-interest, "False" corresponds to the background). I was aggregating multiple binary segmentation arrays to do a majority vote with an implementation that boiled down to the following: >>> pred1, pred2, ..., predN = np.array(..., dtype=np.bool_), np.array(..., dtype=np.bool_), ..., np.array(..., dtype=np.bool_) >>> aggregate = (pred1 + pred2 + ... + predN) / N >>> agg_pred = aggregate >= 0.5 Which returned (1.0 / N) in all indices which had at least one "True" value in a prediction. I assumed that the arrays would be promoted to integers (False -> 0; True -> 1) and added so that agg_pred would hold the majority vote result. But agg_pred was always empty because the maximum value was (1.0 / N) for N > 2. My current "work around" is to remind myself of this discrepancy by importing "builtins" from the standard library and annotating the relevant functions and variables as using the "builtins.bool" to explicitly distinguish it from np.bool_ behavior where applicable, and add checks and/or conversions on top of that. But why not make np.bool_ act like the built-in bool under addition and multiplication and let users use the already existing | and & operations for "logical or" and "logical and"?

4 6

NumPy Development Meeting Wednesday - Triage Focus (NOTE: Earlier for most!)
by Sebastian Berg 21 Mar '22

21 Mar '22

Note: The time is earlier than last ones in UTC (so earlier if you did not just switched to daylight-savings time). The reason was not daylight saving time, but happens to coincide... Hi all, Our bi-weekly triage-focused NumPy development meeting is Wednesday, March 9th at 16:00 UTC (9:00am Pacific Time). Everyone is invited to join in and edit the work-in-progress meeting topics and notes: https://hackmd.io/68i_JvOYQfy9ERiHgXMPvg I encourage everyone to notify us of issues or PRs that you feel should be prioritized, discussed, or reviewed. A calendar to can be found at: https://scientific-python.org/calendars/ (For this week, the calendar is updated yet, but should update before the meeting.) Best regards Sebastian

1 0

Off Topic Question :Career Advice Needed
by Amardeep Singh 20 Mar '22

20 Mar '22

Hi All I am a Tech Lead based in Singapore. I have got 140k offer in Toronto. Just wanted to understand how is the job market for python,c++ roles in Canada for perm and contract roles. Suppose i join now and get permanent residency after 2 years is it easy to make 200k per year or is it something extremely rare. Any one in this list based in Canada?How is the job market in Canada.? You may reply me on amardeepjk(a)gmail.com with your personal experience( if any) since numpy email id is not meant for this purpose. Thanks Amardeep

1 0

Exposing `from_dlpack` to the main namespace
by Sebastian Berg 18 Mar '22

18 Mar '22

Hi, just a brief note that: https://github.com/numpy/numpy/pull/21145 will add `np.from_dlpack` (to import objects implementing the `__dlpack__` dunder [1]. It was added with an underscore mainly due to unlucky timing, IIRC. There may also be some choices that could be different, but I believe they ended up on the restrictive side for now so that shouldn't need to worry us. So this is just a heads-up, since it is an API addition, and I am not sure it was brought up on the mailing list before. Cheers, Sebastian [1] https://dmlc.github.io/dlpack/latest/python_spec.html

4 5

NumPy Newcomers Hour – Thursday, March 10th
by Inessa Pawson 14 Mar '22

14 Mar '22

The next NumPy Newcomers Hour will be held this *Thursday, March 10th at 4 pm UTC*. *Mukulika Pahari* will present her tutorial *Analyzing the impact of the lockdown on air quality in Delhi, India* ( https://numpy.org/numpy-tutorials/content/tutorial-air-quality-analysis.html ). Join us *via Zoom*: https://us02web.zoom.us/j/87192457898 Cheers, Inessa Inessa Pawson NumPy Contributor Experience Lead

1 1

request to remove the numpy-aarch64 package from PyPI
by Ralf Gommers 14 Mar '22

14 Mar '22

Hi all, FYI, I noticed this package that claimed to be maintained by us: https://pypi.org/project/numpy-aarch64/. That's not ours, so I tried to contact the author (no email provided, but guessed the same username on GitHub) and asked to remove it: https://github.com/tomasriv/DNA_Sequence/issues/1. There are a very large number of packages with "numpy" in the name on PyPI, and there's no way we can audit/police that effectively, but if it's a rebuild that pretends like it's official then I think it's worth doing something about. It could contain malicious code for all we know. Cheers, Ralf

5 7

NumPy community meeting – Wednesday, March 16th, 2022 at 18:00 (6 pm) UTC
by Inessa Pawson 14 Mar '22

14 Mar '22

Our next NumPy community meeting is scheduled for Wednesday, March 16th at 18:00 (6 pm) UTC. Join us via Zoom: https://berkeley.zoom.us/j/762261535 As always, everyone is welcome and encouraged to attend. To add to the meeting agenda the topics you’d like to discuss, follow the link: https://hackmd.io/76o-IxCjQX2mOXO_wwkcpg?both Cheers, Inessa Inessa Pawson Contributor Experience Lead | NumPy

1 0

Suggestions/Views on a Numpy-Tutorial Idea Draft.
by bhavuk kalra 11 Mar '22

11 Mar '22

Good Day Everyone in the Mailing List, This message is sent to the mailing list to get valuable insights from everyone present here on whether to write the Draft for a Numpy-Tutorial. I was going through the type of contributions that are required by the community Discussed in - https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html and Numpy Tutorials was one of them. Seeing this, In the past, I had worked on Classifying a fruit such as("apple" "mango") based on specific given characteristics using a scratch implementation of KNN. KNN as an algorithm I feel is relatively simple to learn. Do let me know If I could start working towards this tutorial and It's something that could add value to the tutorials and the community. I also made a .ipynb notebook to refer to what Implementation would be covered in the tutorial. (Note - It is pretty raw, the actual tutorial will be much more thorough. This is to give a general Idea) Link - https://github.com/bhavukkalra/Genetic_Algorithms/blob/main/AI_assign2/KNN.… Regards, Bhavuk Kalra

1 0