Mailman 3 February 2020 - NumPy-Discussion

help translating into Hindi
by Inessa Pawson 27 Feb '20

27 Feb '20

Our collaboration with the students and faculty from the Master’s program in Survey Methodology at the University of Michigan and the University of Maryland at College Park is underway. We are looking for a volunteer to translate the survey questionnaire into Hindi. If you are available, or you know someone who would be interested to help, please leave a comment here: https://github.com/numpy/numpy-surveys/issues/1. -- Every good wish, *Inessa Pawson * Executive Director Albus Code

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NumPy Development Meeting - Triage Focus
by Sebastian Berg 25 Feb '20

25 Feb '20

Hi all, Our bi-weekly triage-focused NumPy development meeting is tomorrow (Wednesday, Februrary 26) at 11 am 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 or simply discussed briefly. Just comment on it so we can label it, or add your PR/issue to this weeks topics for discussion. Best regards, Sebastian

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Re: [Numpy-discussion] New DTypes: Are scalars a central concept in NumPy or not?
by Stefano Miccoli 25 Feb '20

25 Feb '20

The fact that `isinstance(np.float64(1), float)` raises the problem that the current implementation of np.float64 scalars breaks the Liskov substitution principle: `sequence_or_array[round(x)]` works if `x` is a float, but breaks down if x is a np.float64. See https://github.com/numpy/numpy/issues/11810, where the issue is discussed in the broader setting of the semantics of `np.round` vs. python3 `round`. I do not have a strong opinion here, except that if np.float64’s are within the python number hierarchy they should be PEP 3141 compliant (which currently they are not.) Stefano > On 25 Feb 2020, at 00:03, numpy-discussion-request(a)python.org wrote: > > Also, something to think about is that currently numpy scalars satisfy > the property `isinstance(np.float64(1), float)`, i.e they are within the > python numerical type hierarchy. 0d arrays do not have this property. My > proposal above would break this. I'm not sure what to think about > whether this is a good property to maintain or not. > > Cheers, > Allan

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Suggestion: prevent silent downcast in np.full_like
by A T 25 Feb '20

25 Feb '20

Hello, This is my first activity on this mailing list, please let me know if I am doing anything improperly. I recently opened issue #15635 <https://github.com/numpy/numpy/issues/15635>and it was suggested to me that it could be worth discussing here. Here is a summarized code example of how unsafe downcasting in np.full_like() resulted in issues in our scientific toolbox: t0 = 20.5 # We're trying to make a constant-valued "ufunc" temperature = lambda x: np.full_like(x, t0) print(temperature([0.1, 0.7, 2.3])) # [20.5 20.5 20.5] print(temperature(0)) # 20 This is consistent with the documentation (which even gives an example of this unsafe casting), and was obvious to fix once identified. But what seems especially problematic to me is the fact that the code /looks safe, but isn't/. There is no sketchy `dtype=...` to make you think twice about the possibility of downcasting, but it happens all the same. What are your thoughts on this topic? Should a special warning be given? Should the casting rule be made more strict? Alexis THIBAULT

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Scikit-learn in the news.
by Charles R Harris 21 Feb '20

21 Feb '20

Hi All, Just thought I mention a new paper where scikit-learn was used: A Deep Learning Approach to Antibiotic Discovery <https://www.cell.com/cell/fulltext/S0092-8674(20)30102-1#secsectitle0020>. Congratulations to the scikit-learn team. Chuck

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Tensor Developer Summit
by Stefan van der Walt 19 Feb '20

19 Feb '20

Hi all, This has been mentioned on the community calls, but not on the mailing list, so a reminder about the Tensor Developer Summit happening at March in Berkeley: https://xd-con.org/tensor-2020/ We would love to have developers and advanced users of NumPy (or other array libraries with Python interfaces) attend. Registration closes 20 February. Best regards, Stéfan

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numpy_financial functions
by Jordan 18 Feb '20

18 Feb '20

I teach finance at IU Bloomington and use the numpy_financial module pretty heavily. I've written a couple of functions for bond math for my own use (duration, convexity, forward rates, etc.). Is there any appetite for expanding numpy_financial beyond the core Excel functions? Is there a point-person for numpy_financial with whom I could correspond about contributing? Thanks, Jordan

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NumPy Community Meeting Wednesday, Feb. 19
by Sebastian Berg 18 Feb '20

18 Feb '20

Hi all, There will be a NumPy Community meeting Wednesday February 19 at 11 am Pacific Time. Everyone is invited to join in and edit the work-in- progress meeting topics and notes: https://hackmd.io/76o-IxCjQX2mOXO_wwkcpg?both Best wishes Sebastian

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recent changes in np.maximum.accumulate ?
by josef.pktd＠gmail.com 18 Feb '20

18 Feb '20

I'm trying to track down test failures of statsmodels against recent master dev versions of numpy and scipy. The core computation is the following in one set of tests that fail pvals_corrected_raw = pvals * np.arange(ntests, 0, -1) pvals_corrected = np.maximum.accumulate(pvals_corrected_raw) this numpy version numpy-1.19.0.dev0%2B20200214184618_1f9ab28-cp38-cp38-manylinux2010_x86_64.whl is in the test run with failures (the first time statsmodel master failed) the previous version in the test runs didn't have these failures numpy-1.19.0.dev0%2B20200212232857_af0dfce-cp38-cp38-manylinux1_x86_64.whl I'm right now just fishing for candidates for the failures. And I'm not running any dev versions on my computer. Were there any recent changes that affect np.maximum.accumulate? Josef

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Re: [Numpy-discussion] recent changes in np.maximum.accumulate ?
by Kevin Sheppard 18 Feb '20

18 Feb '20

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