Mailman 3 September 2021 - NumPy-Discussion

new searchable mailing list archives
by Ralf Gommers 22 Sep '21

22 Sep '21

Hi all, Thanks to the friendly folks who maintain the python.org mailing lists, we now have nice searchable mailing list archives for NumPy and SciPy: https://mail.python.org/archives/list/numpy-discussion@python.org/ https://mail.python.org/archives/list/scipy-dev@python.org/ https://mail.python.org/archives/list/scipy-user@python.org/ Cheers, Ralf

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[academic study] how do OSS developers respond to such phenomena as partially or wrongly implemented functionality?
by nicole novielli 20 Sep '21

20 Sep '21

Hello, We're a team of Software Engineering researchers investigating technical debt. Technical debt refers to "not quite right code which we postpone making it right” (Cunningham) and this may often introduce a cost. More specifically, the aim of this study is to gather information on how open source developers respond to specific source code phenomena such as partially or wrongly implemented functionality. With the results of this study we aim to create a better understanding of how developers annotate issues in source code. These results will be used to support prioritisation of maintenance activities in software systems. The survey data will be collected, analyzed and stored so that research questions can be answered and the results can be published. Since the survey is anonymous, no personal information is collected. For the same reason we cannot modify or remove answers upon request. If you contribute code to this project you can help us out by filling out the following survey: https://forms.office.com/r/xRg92faiNd. The survey should take you at most 15 minutes. Thanks in advance, Nicole Novielli (University of Bari, Italy) also on behalf of Nathan Cassee, Massimiliano Di Penta, Alexander Serebrenik, and Fiorella Zampetti

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deprecating float(x) for ndim > 0
by Nico Schlömer 18 Sep '21

18 Sep '21

Hi everyone, This is seeking input on PR [1] which I've worked on with @eric-wieser and @seberg. It deprecates ``` float(x) ``` if `x` is an array of ndim > 0. (It works with all arrays of size 1 right now.) This aligns the behavior of float() on ndarrays with float() on lists which already fails today. It also deprecates the implicit conversion to float in assignment expressions like ``` a = np.array([1, 2, 3]) a[0] = [5] # deprecated, should be a[0] = 5 ``` In general, the PR makes numpy a tad bit stricter on how it treats scalars vs. single-item arrays. The change also prevents the #1 wrong usage of float(), namely for extracting the scalar value from an array. One should rather use `x[0]` or `x.item()` to that which doesn't convert the value to a Python float. To estimate the impact of the PR, I looked at major numpy dependents like matplotlib, scipy, pandas etc., and of course numpy itself. Except scipy, all projects were virtually clean to start with. Scipy needed some changes for all tests to pass without warning, and all of the changes were improvements. In particular, the deprecation motivates users to use actual scalars when scalars are needed, e.g., in the case of scipy, as the return value of a goal functional. It'd be great if you could try the branch against your own project and let us know (here or in the PR) about and problems that you might have. Thanks! Nico [1] https://github.com/numpy/numpy/pull/10615 [2] https://github.com/numpy/numpy/issues/10404

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npreadtext: `numpy.loadtxt` in C
by Ross Barnowski 16 Sep '21

16 Sep '21

Hi all, This is to announce [`npreadtext`](https://github.com/BIDS-numpy/npreadtext), a drop-in replacement for `numpy.loadtxt` written in C for improved performance. We are now at feature parity with `loadtxt`, and would greatly appreciate your feedback & testing. We hope eventually to include `npreadtext` in NumPy itself. ## Installation `npreadtext` has been tested with NumPy v1.18 and higher and can be installed using: ``` python -m pip install numpy python -m pip install git+git://github.com/BIDS-numpy/npreadtext ``` To enable the C-accelerated version of `np.loadtxt`, monkey-patch NumPy: ```python >>> import numpy as np >>> from npreadtxt import monkeypatch_numpy ``` This replaces `np.loadtxt` with `npreadtext._loadtxt`. ## Feedback You may leave comments here or file issues on the [project issue tracker]( https://github.com/BIDS-numpy/npreadtext/issues). Please also share text files that strain or break the reader. ## Benchmarks Preliminary benchmarks show a significant improvement in performance: ``` python runtests.py --bench-compare monkeypatch-npreadtext bench_io npreadtext np.loadtxt speedup function + 7.74±0.04ms 146±0.8ms 18.85 bench_io.LoadtxtCSVStructured.time_loadtxt_csv_struct_dtype + 9.67±0.1ms 181±0.6ms 18.67 bench_io.LoadtxtCSVdtypes.time_loadtxt_dtypes_csv('int64', 100000) + 969±10μs 17.9±0.1ms 18.48 bench_io.LoadtxtCSVdtypes.time_loadtxt_dtypes_csv('int64', 10000) + 950±7μs 14.6±0.04ms 15.39 bench_io.LoadtxtCSVdtypes.time_loadtxt_dtypes_csv('int32', 10000) + 9.65±0.03ms 146±0.2ms 15.13 bench_io.LoadtxtCSVdtypes.time_loadtxt_dtypes_csv('int32', 100000) + 11.8±0.06ms 141±0.3ms 11.96 bench_io.LoadtxtCSVdtypes.time_loadtxt_dtypes_csv('float32', 100000) + 11.9±0.1ms 141±0.3ms 11.88 bench_io.LoadtxtCSVdtypes.time_loadtxt_dtypes_csv('float64', 100000) + 12.6±0.1ms 150±0.6ms 11.85 bench_io.LoadtxtCSVdtypes.time_loadtxt_dtypes_csv('complex128', 100000) + 1.18±0.01ms 13.9±0.1ms 11.74 bench_io.LoadtxtCSVdtypes.time_loadtxt_dtypes_csv('float64', 10000) + 1.19±0.01ms 13.9±0.09ms 11.68 bench_io.LoadtxtCSVdtypes.time_loadtxt_dtypes_csv('float32', 10000) + 1.27±0ms 14.7±0.06ms 11.64 bench_io.LoadtxtCSVdtypes.time_loadtxt_dtypes_csv('complex128', 10000) + 12.4±0.06ms 140±0.6ms 11.28 bench_io.LoadtxtCSVComments.time_comment_loadtxt_csv(100000) + 1.22±0.02ms 13.8±0.09ms 11.26 bench_io.LoadtxtCSVComments.time_comment_loadtxt_csv(10000) + 20.8±0.2μs 194±0.5μs 9.32 bench_io.LoadtxtCSVdtypes.time_loadtxt_dtypes_csv('int64', 100) + 20.4±0.2μs 162±0.3μs 7.97 bench_io.LoadtxtCSVdtypes.time_loadtxt_dtypes_csv('int32', 100) + 1.04±0ms 8.17±0.08ms 7.84 bench_io.LoadtxtUseColsCSV.time_loadtxt_usecols_csv([1, 3, 5, 7]) + 884±2μs 6.79±0.02ms 7.68 bench_io.LoadtxtUseColsCSV.time_loadtxt_usecols_csv([1, 3]) + 1.56±0.01ms 12.0±0.05ms 7.68 bench_io.LoadtxtCSVdtypes.time_loadtxt_dtypes_csv('object', 10000) + 16.1±0.05ms 122±0.3ms 7.56 bench_io.LoadtxtCSVdtypes.time_loadtxt_dtypes_csv('object', 100000) + 23.4±0.04μs 163±0.9μs 6.94 bench_io.LoadtxtCSVdtypes.time_loadtxt_dtypes_csv('complex128', 100) + 22.6±0.09μs 153±0.2μs 6.76 bench_io.LoadtxtCSVdtypes.time_loadtxt_dtypes_csv('float32', 100) + 22.9±0.5μs 154±0.7μs 6.72 bench_io.LoadtxtCSVdtypes.time_loadtxt_dtypes_csv('float64', 100) + 22.8±0.5μs 150±0.8μs 6.58 bench_io.LoadtxtCSVComments.time_comment_loadtxt_csv(100) + 809±8μs 5.10±0.02ms 6.30 bench_io.LoadtxtUseColsCSV.time_loadtxt_usecols_csv(2) + 7.31±0.01ms 42.0±0.08ms 5.75 bench_io.LoadtxtCSVDateTime.time_loadtxt_csv_datetime(20000) + 748±2μs 4.11±0.04ms 5.50 bench_io.LoadtxtCSVDateTime.time_loadtxt_csv_datetime(2000) + 26.0±0.2μs 131±0.3μs 5.02 bench_io.LoadtxtCSVdtypes.time_loadtxt_dtypes_csv('object', 100) + 87.3±0.4μs 436±1μs 5.00 bench_io.LoadtxtCSVDateTime.time_loadtxt_csv_datetime(200) + 2.09±0.01ms 10.1±0.04ms 4.86 bench_io.LoadtxtReadUint64Integers.time_read_uint64(10000) + 2.09±0ms 10.1±0.04ms 4.83 bench_io.LoadtxtReadUint64Integers.time_read_uint64_neg_values(10000) + 215±0.5μs 1.03±0ms 4.82 bench_io.LoadtxtReadUint64Integers.time_read_uint64_neg_values(1000) + 217±0.9μs 1.02±0ms 4.72 bench_io.LoadtxtReadUint64Integers.time_read_uint64(1000) + 123±0.6μs 580±3μs 4.71 bench_io.LoadtxtReadUint64Integers.time_read_uint64_neg_values(550) + 124±0.8μs 573±4μs 4.63 bench_io.LoadtxtReadUint64Integers.time_read_uint64(550) + 4.15±0.01ms 14.4±0.05ms 3.46 bench_io.LoadtxtCSVdtypes.time_loadtxt_dtypes_csv('str', 10000) + 58.6±0.1ms 195±0.8ms 3.33 bench_io.LoadtxtCSVSkipRows.time_skiprows_csv(10000) + 41.8±0.1ms 139±1ms 3.33 bench_io.LoadtxtCSVdtypes.time_loadtxt_dtypes_csv('str', 100000) + 64.6±0.09ms 215±1ms 3.32 bench_io.LoadtxtCSVSkipRows.time_skiprows_csv(500) + 64.9±0.2ms 215±2ms 3.30 bench_io.LoadtxtCSVSkipRows.time_skiprows_csv(0) + 55.0±0.5μs 154±0.4μs 2.81 bench_io.LoadtxtCSVdtypes.time_loadtxt_dtypes_csv('str', 100) + 23.9±0.1μs 60.1±1μs 2.51 bench_io.LoadtxtCSVDateTime.time_loadtxt_csv_datetime(20) + 12.1±0.2μs 29.4±0.2μs 2.44 bench_io.LoadtxtCSVdtypes.time_loadtxt_dtypes_csv('int64', 10) + 12.0±0.05μs 26.2±0.2μs 2.18 bench_io.LoadtxtCSVdtypes.time_loadtxt_dtypes_csv('int32', 10) + 12.5±0.08μs 26.1±0.09μs 2.08 bench_io.LoadtxtCSVdtypes.time_loadtxt_dtypes_csv('complex128', 10) + 12.3±0.04μs 24.9±0.4μs 2.02 bench_io.LoadtxtCSVdtypes.time_loadtxt_dtypes_csv('float64', 10) + 12.3±0.1μs 24.8±0.2μs 2.02 bench_io.LoadtxtCSVdtypes.time_loadtxt_dtypes_csv('float32', 10) + 12.2±0.04μs 24.5±0.1μs 2.01 bench_io.LoadtxtCSVComments.time_comment_loadtxt_csv(10) + 13.3±0.1μs 23.4±0.1μs 1.76 bench_io.LoadtxtCSVdtypes.time_loadtxt_dtypes_csv('object', 10) + 18.5±0.3μs 25.6±0.5μs 1.39 bench_io.LoadtxtCSVdtypes.time_loadtxt_dtypes_csv('str', 10) ``` The repository includes [procedures for running benchmarks locally]( https://github.com/BIDS-numpy/npreadtext#benchmarking).

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Re: [Numpy-discussion] deprecating float(x) for ndim > 0
by george trojan 16 Sep '21

16 Sep '21

Responding to the post by nico.schloemer(a)gmail.com (I subscribe to the digest). I just wrote the following code: twb = scipy.optimize.fsolve(phi, tdb, args=(tdb, p, w, hd_tdb, hg_tdb), xtol=1e-8) tdb, p, w, hd_tdb, hg_tdb twb.shape print("wet-bulb temperature {:.5f} [deg K]".format(float(twb))) The output is (313.15, 101325.0, 0.009200033532084696, 40182.343155896095, 2573510.322137241) (1,) wet-bulb temperature 295.17583 [deg K] All arguments are floats, the function phi returns float as well. I did expect the output to be float. Instead I got a 1d array. Were my expectations wrong? The print statement would stop working after deprecation. George

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Re: [Numpy-discussion] deprecating float(x) for ndim > 0
by george trojan 16 Sep '21

16 Sep '21

To Andrew Nelson: > In the return section for fsolve the documentation states that the return > value, `x`, is an `ndarray`. True, my bad. There is another issue with `fsolve`: it implicitly changes the argument passed to `func`. Consider def func(x): # x = float(x) if not np.isscalar(x) and x.shape != (): raise ValueError('The argument must be a number or a 0-d array') return x - 1 fsolve(func, np.array(2.0)) ---------------------------------------------------------------------------ValueError Traceback (most recent call last)/tmp/ipykernel_1017360/2091764495.py in <module> 5 return x - 1 6 ----> 7 fsolve(func, np.array(2.0)) . . . ValueError: The argument must be a number or a 0-d array I had the commented out line in my code (in real life I am calling a library function that accepts only numeric types, not arrays). Changing this to line to x = x.item() makes sense. The documentation states that `func` takes at least one (possibly vector) argument. It must take a vector.

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Re: [Numpy-discussion] graphic design contribution to NumPy
by Inessa Pawson 16 Sep '21

16 Sep '21

Hi, Vasiliy! It would be great if the infographic embraces the flat design principles and echoes the style of other graphic elements of the website. Let’s continue this conversation on GitHub, in the comments for the issue #435 <https://github.com/numpy/numpy.org/issues/435>. On Tue, Sep 14, 2021 at 12:00 PM <numpy-discussion-request(a)python.org> wrote: > Send NumPy-Discussion mailing list submissions to > numpy-discussion(a)python.org > > To subscribe or unsubscribe via the World Wide Web, visit > https://mail.python.org/mailman/listinfo/numpy-discussion > or, via email, send a message with subject or body 'help' to > numpy-discussion-request(a)python.org > > You can reach the person managing the list at > numpy-discussion-owner(a)python.org > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of NumPy-Discussion digest..." > Today's Topics: > > 1. graphic design contribution (Holam Gadol) > > > > ---------- Forwarded message ---------- > From: Holam Gadol <holamgadol(a)gmail.com> > To: Discussion of Numerical Python <numpy-discussion(a)python.org> > Cc: > Bcc: > Date: Mon, 13 Sep 2021 23:11:07 +0300 > Subject: [Numpy-discussion] graphic design contribution > Thank you for replying, Inessa. > > Refining infographics from the issue #435 > <https://github.com/numpy/numpy.org/issues/435> looks interesting. > > Do you have a particular style for them? I saw the icons were redrawn in > material design. Should infographics follow the same style? > > Best wishes, > Vasiliy > > > пн, 13 сент. 2021 г. в 06:11, Inessa Pawson <albuscode(a)gmail.com>: > >> Thank you for offering your help, Holam! >> We have quite a few options for you. If you’d like to help us improve >> numpy.org, this is the latest discussion on this topic: >> https://github.com/numpy/numpy.org/issues/342 . We could also apply your >> graphic design skills to enhance our documentation and educational >> materials. >> Let me know what you’d like to take on, and I will send you more info >> about it. >> >> On Sat, Sep 11, 2021 at 12:04 PM <numpy-discussion-request(a)python.org> >> wrote: >> >>> Send NumPy-Discussion mailing list submissions to >>> numpy-discussion(a)python.org >>> >>> To subscribe or unsubscribe via the World Wide Web, visit >>> https://mail.python.org/mailman/listinfo/numpy-discussion >>> or, via email, send a message with subject or body 'help' to >>> numpy-discussion-request(a)python.org >>> >>> You can reach the person managing the list at >>> numpy-discussion-owner(a)python.org >>> >>> When replying, please edit your Subject line so it is more specific >>> than "Re: Contents of NumPy-Discussion digest..." >>> Today's Topics: >>> >>> 1. graphic design contribution (Holam Gadol) >>> >>> >>> >>> ---------- Forwarded message ---------- >>> From: Holam Gadol <holamgadol(a)gmail.com> >>> To: numpy-discussion(a)python.org >>> Cc: >>> Bcc: >>> Date: Sat, 11 Sep 2021 16:05:43 +0300 >>> Subject: [Numpy-discussion] graphic design contribution >>> Greetings to everyone! >>> >>> I`de like to help the numpy project with graphic design. >>> >>> What are the first steps to start with? >>> >>> There are issues about documentation and code on github, but how about >>> graphics? >>> _______________________________________________ >>> NumPy-Discussion mailing list >>> NumPy-Discussion(a)python.org >>> https://mail.python.org/mailman/listinfo/numpy-discussion >>> >> >> >> -- >> Every good wish, >> *Inessa Pawson* >> Executive Director >> Albus Code >> inessa(a)albuscode.org >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion(a)python.org >> https://mail.python.org/mailman/listinfo/numpy-discussion >> > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion(a)python.org > https://mail.python.org/mailman/listinfo/numpy-discussion > -- Every good wish, *Inessa Pawson* Executive Director Albus Code inessa(a)albuscode.org

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NumPy Community Meeting Wednesday
by Sebastian Berg 15 Sep '21

15 Sep '21

Hi all, There will be a NumPy Community meeting Wednesday September 15th at 20:00 UTC. Everyone is invited and encouraged to join in and edit the work-in-progress meeting topics and notes at: https://hackmd.io/76o-IxCjQX2mOXO_wwkcpg?both Best wishes Sebastian

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Re: [Numpy-discussion] NumPy-Discussion Digest, Vol 180, Issue 9
by Inessa Pawson 14 Sep '21

14 Sep '21

Thank you for offering your help, Holam! We have quite a few options for you. If you’d like to help us improve numpy.org, this is the latest discussion on this topic: https://github.com/numpy/numpy.org/issues/342 . We could also apply your graphic design skills to enhance our documentation and educational materials. Let me know what you’d like to take on, and I will send you more info about it. On Sat, Sep 11, 2021 at 12:04 PM <numpy-discussion-request(a)python.org> wrote: > Send NumPy-Discussion mailing list submissions to > numpy-discussion(a)python.org > > To subscribe or unsubscribe via the World Wide Web, visit > https://mail.python.org/mailman/listinfo/numpy-discussion > or, via email, send a message with subject or body 'help' to > numpy-discussion-request(a)python.org > > You can reach the person managing the list at > numpy-discussion-owner(a)python.org > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of NumPy-Discussion digest..." > Today's Topics: > > 1. graphic design contribution (Holam Gadol) > > > > ---------- Forwarded message ---------- > From: Holam Gadol <holamgadol(a)gmail.com> > To: numpy-discussion(a)python.org > Cc: > Bcc: > Date: Sat, 11 Sep 2021 16:05:43 +0300 > Subject: [Numpy-discussion] graphic design contribution > Greetings to everyone! > > I`de like to help the numpy project with graphic design. > > What are the first steps to start with? > > There are issues about documentation and code on github, but how about > graphics? > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion(a)python.org > https://mail.python.org/mailman/listinfo/numpy-discussion > -- Every good wish, *Inessa Pawson* Executive Director Albus Code inessa(a)albuscode.org

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graphic design contribution
by Holam Gadol 11 Sep '21

11 Sep '21

Greetings to everyone! I`de like to help the numpy project with graphic design. What are the first steps to start with? There are issues about documentation and code on github, but how about graphics?

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