Hi, Friedrich > There seems to be missing an "a" before "more". Thank you. Fixed. This is a draft. It will be (more or less) professionally proofread thereafter.
> on my machine it runs:: Which OS does your machine run on? > FloatingPointError written instead of RuntimeWarning This is most certainly a typo. Thanks. > *only once*: Good point! Added. > So, unclarity resolved, but maybe I am not the only one stumbling over this. Ok, I'll think how to improve readability here. > Maybe the idiom ``>>> c = numpy.int64(2 ** 63 - 1)`` can be used? It was there in one of ther earlier versions of the article, but np.array fitted the narrative thread better mostly this very reason you provided: although It's good to know that scalars can constructed this way, noone does it in real-life use cases. Thank you for your feedback! Looking forward to reading the next part of the reivew. Best regards, Lev On Sun, Dec 26, 2021 at 8:45 PM Michael Siebert <michael.sieber...@gmail.com> wrote: > Hey Lev, > > I‘ve forgotten to mention my MacBook M1, > it‘s also int64 there. > > Python on Windows is and is supposed to be, as far as I get it, a dying > platform. A billion things are broken there (HDF comes to my mind) and it > seems even Microsoft wants developers to move away from native Windows with > their introduction of WSL (Windows Subsystem for Linux). Its latest > version, WSL2 even comes with an actual Linux kernel and since Windows 11, > it has support for graphical applications (Xorg) out of the box. With > Visual Studio Code (also Microsoft) and it’s remote capabilities, one does > not even feel a difference between developing in an Ubuntu in a WSL in > Windows and an actual Ubuntu. > > Considering the „traditional“ C datatypes, fixed types and prioritizing > them in Numpy documentation, that‘s what my issue (see below) is about. I > think they have summarized it nicely in > > https://matt.sh/howto-c > > Best regards, Michael > > On 26. Dec 2021, at 13:49, Lev Maximov <lev.maxi...@gmail.com> wrote: > > > Dear Michael, > > Thank you for your feedback! > > I've fixed the x86_64 typo. > > I'll think how to reformulate the int32 part. I work on debian x86_64 and > windows 10 64bit. Constructing an array with np.array([1,2,3]) as well as > np.array([1,2,3], dtype=np.int_) gives me int64 dtype on linux, and int32 > on windows. > > As suggested by Matti, I've put the rst source (and images) into a > separate github repository > > https://github.com/axil/numpy-data-types > > PRs are welcome. My primary concern is to exclude serious typos/mistakes > that might mislead/harm the readers if used. > > My personal preference is towards explicit width types like np.int32, but > from reading the docs I have a feeling there's a trend of migrating towards > the c-style notation. > > Best regards, > Lev > > On Sun, Dec 26, 2021 at 7:05 PM Michael Siebert < > michael.sieber...@gmail.com> wrote: > >> Dear Lev, >> >> thank you a lot! Something like this should be part of the Numpy >> documentation. I like the diagram, looks very nice! Also, I’ve opened an >> issue regarding data types >> >> https://github.com/numpy/numpy/issues/20662 >> >> Some feedback from my side: >> >> 1. When calling numpy.array([1,2,3,4]) it gives me an int64 data type >> most of the time (two x86_64 systems, one arm64 system). The only time I’ve >> got int32 was on a Raspberry Pi, which is a software limitation, since the >> CPU is 64 bit and they have even replaced their so-far 32bit only Raspberry >> Pi Zero with a 64bit version (yes, one day Raspberry OS with 64 bit might >> actually become the default!). I don’t know what machine you are working >> on, but int64 should be the default. >> 2. x64 refers to the obsolete Intel Itanium architecture (mentioned >> once). Should be x86_64. >> 3. np.errstate looks nice, I could use that for my pull request as well. >> >> Many thanks & best regards, Michael >> >> >> On 25. Dec 2021, at 10:02, Lev Maximov <lev.maxi...@gmail.com> wrote: >> >> Hi everyone, >> >> I'm almost done with the article about numpy types – something I haven't >> covered in Numpy Illustrated. >> >> Would someone please have a look to confirm I haven't written anything >> anti-climatic there? >> >> https://axil.github.io/numpy-data-types.html >> >> -- >> Best regards, >> Lev >> >> PS Earlier today I've mistakenly sent an email with the wrong link. >> _______________________________________________ >> NumPy-Discussion mailing list -- numpy-discussion@python.org >> To unsubscribe send an email to numpy-discussion-le...@python.org >> https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ >> Member address: michael.sieber...@gmail.com >> >> >> _______________________________________________ >> NumPy-Discussion mailing list -- numpy-discussion@python.org >> To unsubscribe send an email to numpy-discussion-le...@python.org >> https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ >> Member address: lev.maxi...@gmail.com >> > _______________________________________________ > NumPy-Discussion mailing list -- numpy-discussion@python.org > To unsubscribe send an email to numpy-discussion-le...@python.org > https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ > Member address: michael.sieber...@gmail.com > > _______________________________________________ > NumPy-Discussion mailing list -- numpy-discussion@python.org > To unsubscribe send an email to numpy-discussion-le...@python.org > https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ > Member address: lev.maxi...@gmail.com >
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