Mailman 3 April 2021 - NumPy-Discussion

mixed mode arithmetic
by Neal Becker July 11, 2023

July 11, 2023

I've been browsing the numpy source. I'm wondering about mixed-mode arithmetic on arrays. I believe the way numpy handles this is that it never does mixed arithmetic, but instead converts arrays to a common type. Arguably, that might be efficient for a mix of say, double and float. Maybe not. But for a mix of complex and a scalar type (say, CDouble * Double), it's clearly suboptimal in efficiency. So, do I understand this correctly? If so, is that something we should improve?

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Invalid value encoutered : how to prevent numpy.where to do this?
by Eric Emsellem Feb. 18, 2023

Feb. 18, 2023

Dear all, I have a code using lots of "numpy.where" to make some constrained calculations as in: data = arange(10) result = np.where(data == 0, 0., 1./data) # or data1 = arange(10) data2 = arange(10)+1.0 result = np.where(data1 > data2, np.sqrt(data1-data2), np.sqrt(data2-data2)) which then produces warnings like: /usr/bin/ipython:1: RuntimeWarning: invalid value encountered in sqrt or for the first example: /usr/bin/ipython:1: RuntimeWarning: divide by zero encountered in divide How … [View More]

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Documentation Team meeting - Monday June 8th
by Melissa Mendonça Dec. 3, 2022

Dec. 3, 2022

Hi all! A reminder that on Monday, June 8, we have another documentation team meeting at 3PM UTC**. If you wish to join on Zoom, you need to use this link https://zoom.us/j/420005230 Here's the permanent hackmd document with the meeting notes: https://hackmd.io/oB_boakvRqKR-_2jRV-Qjg <https://www.google.com/url?q=https%3A%2F%2Fhackmd.io%2FoB_boakvRqKR-_2jRV-Q…> Hope to see you around (especially if you want to introduce yourself or discuss ideas for Google Season of Docs). ** You … [View More]

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[Feature Request] Add alias of np.concatenate as np.concat
by Iordanis Fostiropoulos May 10, 2022

May 10, 2022

In regard to Feature Request: https://github.com/numpy/numpy/issues/16469 It was suggested to sent to the mailing list. I think I can make a strong point as to why the support for this naming convention would make sense. Such as it would follow other frameworks that often work alongside numpy such as tensorflow. For backward compatibility, it can simply be an alias to np.concatenate I often convert portions of code from tf to np, it is as simple as changing the base module from tf to np. e.g. … [View More]

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Re: [Numpy-discussion] example reading binary Fortran file
by Neil Martinsen-Burrell July 22, 2021

July 22, 2021

David Froger <david.froger.info <at> gmail.com> writes: > Hy,My question is about reading Fortran binary file (oh no this question > again...) I've posted this before, but I finally got it cleaned up for the Cookbook. For this purpose I use a subclass of file that has methods for reading unformatted Fortran data. See http://www.scipy.org/Cookbook/FortranIO/FortranFile. I'd gladly see this in numpy or scipy somewhere, but I'm not sure where it belongs. > program … [View More]makeArray > implicit none > integer,parameter:: nx=10,ny=20 > real(4),dimension(nx,ny):: ux,uy,p > integer :: i,j > open(11,file='uxuyp.bin',form='unformatted') > do i = 1,nx > do j = 1,ny > ux(i,j) = real(i*j) > uy(i,j) = real(i)/real(j) > p (i,j) = real(i) + real(j) > enddo > enddo > write(11) ux,uy > write(11) p > close(11) > end program makeArray When I run the above program compiled with gfortran on my Intel Mac, I can read it back with:: >>> import numpy as np >>> from fortranfile import FortranFile >>> f=FortranFile('uxuyp.bin', endian='<') >>> uxuy = f.readReals(prec='f') # 'f' for default reals >>> len(uxuy) 400 >>> ux = np.array(uxuy[:200]).reshape((20,10)).T >>> uy = np.array(uxuy[200:]).reshape((20,10)).T >>> p = f.readReals('f').reshape((20,10)).T >>> ux array([[ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20.], [ 2., 4., 6., 8., 10., 12., 14., 16., 18., 20., 22., 24., 26., 28., 30., 32., 34., 36., 38., 40.], [ 3., 6., 9., 12., 15., 18., 21., 24., 27., 30., 33., 36., 39., 42., 45., 48., 51., 54., 57., 60.], [ 4., 8., 12., 16., 20., 24., 28., 32., 36., 40., 44., 48., 52., 56., 60., 64., 68., 72., 76., 80.], [ 5., 10., 15., 20., 25., 30., 35., 40., 45., 50., 55., 60., 65., 70., 75., 80., 85., 90., 95., 100.], [ 6., 12., 18., 24., 30., 36., 42., 48., 54., 60., 66., 72., 78., 84., 90., 96., 102., 108., 114., 120.], [ 7.,Proxy-Connection: keep-alive Cache-Control: max-age=0 14., 21., 28., 35., 42., 49., 56., 63., 70., 77., 84., 91., 98., 105., 112., 119., 126., 133., 140.], [ 8., 16., 24., 32., 40., 48., 56., 64., 72., 80., 88., 96., 104., 112., 120., 128., 136., 144., 152., 160.], [ 9., 18., 27., 36., 45., 54., 63., 72., 81., 90., 99., 108., 117., 126., 135., 144., 153., 162., 171., 180.], [ 10., 20., 30., 40., 50., 60., 70., 80., 90., 100., 110., 120., 130., 140., 150., 160., 170., 180., 190., 200.]]) >>> uy array([[ 1. , 0.5 , 0.33333334, 0.25 , 0.2 , 0.16666667, 0.14285715, 0.125 , 0.11111111, 0.1 , 0.09090909, 0.08333334, 0.07692308, 0.07142857, 0.06666667, 0.0625 , 0.05882353, 0.05555556, 0.05263158, 0.05 ], [ 2. , 1. , 0.66666669, 0.5 , 0.40000001, 0.33333334, 0.2857143 , 0.25 , 0.22222222, 0.2 , 0.18181819, 0.16666667, 0.15384616, 0.14285715, 0.13333334, 0.125 , 0.11764706, 0.11111111, 0.10526316, 0.1 ], [ 3. , 1.5 , 1. , 0.75 , 0.60000002, 0.5 , 0.42857143, 0.375 , 0.33333334, 0.30000001, 0.27272728, 0.25 , 0.23076923, 0.21428572, 0.2 , 0.1875 , 0.17647059, 0.16666667, 0.15789473, 0.15000001], [ 4. , 2. , 1.33333337, 1. , 0.80000001, 0.66666669, 0.5714286 , 0.5 , 0.44444445, 0.40000001, 0.36363637, 0.33333334, 0.30769232, 0.2857143 , 0.26666668, 0.25 , 0.23529412, 0.22222222, 0.21052632, 0.2 ], [ 5. , 2.5 , 1.66666663, 1.25 , 1. , 0.83333331, 0.71428573, 0.625 , 0.55555558, 0.5 , 0.45454547, 0.41666666, 0.38461539, 0.35714287, 0.33333334, 0.3125 , 0.29411766, 0.27777779, 0.2631579 , 0.25 ], [ 6. , 3. , 2. , 1.5 , 1.20000005, 1. , 0.85714287, 0.75 , 0.66666669, 0.60000002, 0.54545456, 0.5 , 0.46153846, 0.42857143, 0.40000001, 0.375 , 0.35294119, 0.33333334, 0.31578946, 0.30000001], [ 7. , 3.5 , 2.33333325, 1.75 , 1.39999998, 1.16666663, 1. , 0.875 , 0.77777779, 0.69999999, 0.63636363, 0.58333331, 0.53846157, 0.5 , 0.46666667, 0.4375 , 0.41176471, 0.3888889 , 0.36842105, 0.34999999], [ 8. , 4. , 2.66666675, 2. , 1.60000002, 1.33333337, 1.14285719, 1. , 0.8888889 , 0.80000001, 0.72727275, 0.66666669, 0.61538464, 0.5714286 , 0.53333336, 0.5 , 0.47058824, 0.44444445, 0.42105263, 0.40000001], [ 9. , 4.5 , 3. , 2.25 , 1.79999995, 1.5 , 1.28571427, 1.125 , 1. , 0.89999998, 0.81818181, 0.75 , 0.69230771, 0.64285713, 0.60000002, 0.5625 , 0.52941179, 0.5 , 0.47368422, 0.44999999], [ 10. , 5. , 3.33333325, 2.5 , 2. , 1.66666663, 1.42857146, 1.25 , 1.11111116, 1. , 0.90909094, 0.83333331, 0.76923078, 0.71428573, 0.66666669, 0.625 , 0.58823532, 0.55555558, 0.52631581, 0.5 ]]) >>> p array([[ 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21.], [ 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22.], [ 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23.], [ 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24.], [ 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.], [ 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26.], [ 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27.], [ 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28.], [ 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29.], [ 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30.]]) Note that you have to provide the shape information for ux and uy because fortran writes them together as a stream of 400 numbers. -Neil [View Less]

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Add smallest_normal and smallest_subnormal attributes to finfo
by Stephannie Jiménez Gacha July 19, 2021

July 19, 2021

Good afternoon, Given the discussions happened in the Data API consortium when looking into the attributes of `finfo` used in the wild, we found that `tiny` is used regularly but in a good amount of cases not for its intended purpose but rather as "just give me a small number". Following this we are proposing the addition of `smallest_normal` and `smallest_subnormal` attributes. Personally, I think that the `tiny` name is a little bit odd and misleading, so it will be great to leave that as an … [View More]

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NEP 42 status
by Lee Johnston May 11, 2021

May 11, 2021

Is the work on NEP 42 custom DTypes far enough along to experiment with? Lee

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GSoD 2021 - Statement of Interest
by Mahesh S May 10, 2021

May 10, 2021

Hello, I am Mahesh from India. I am interested in doing Google Season of Docs with NumPy on the project HIGH-LEVEL RESTRUCTURING AND END-USER FOCUS - NumPy I have past experience in documentation writing with WordPress and have completed *Google Summer of Code 2018 *with KDE. I have been an open source enthusiast and contributor since 2017.My past experience with coding ,code documentation , understanding code bases ,will help achieve this task without much input from your end. I have about … [View More]

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Fwd: PhD position in the Netherlands; solar energy/Python
by Dr. Mark Alexander Mikofski PhD April 30, 2021

April 30, 2021

Hello, sharing a PhD position opening at Utrecht in Netherlands. Read more below... ---------- Forwarded message --------- From: Mark Mikofski <bwanamarko(a)gmail.com> Date: Fri, Apr 30, 2021, 12:29 PM Subject: Fwd: PhD position in the Netherlands; solar energy/Python To: Mark Alexander Mikofski <mikofski(a)berkeley.edu> PhD positions with Wilfried van Sark at Utrecht Netherlands ---------- Forwarded message --------- From: abedw...(a)googlemail.com <abedwayyad(a)googlemail.… [View More]com> Date: Monday, April 19, 2021 at 9:22:00 AM UTC-7 Subject: PhD position in the Netherlands; solar energy/Python To: pvlib-python <pvlib-python(a)googlegroups.com> Hello all, I did my MSc with Wilfried at Utrecht (the Netherlands). He's a truly wonderful mentor with an amazing and broad grasp of the knowledge. 10/10 would recommend doing a PhD there if you or someone you know would like to develop solar forecasting models. They are looking for pythonistas. All the best, Abed ---------- Forwarded message --------- From: dr. Wilfried van Sark - W.G.J.H....(a)uu.nl <blackboar...(a)uu.nl> Date: Wed, Apr 14, 2021 at 4:11 AM Subject: Master Energy Science: Looking for PhD/junior researchers To: Dear all, I am looking for talented and motivated junior researchers that would be interested to work in two projects: 1) ROBUST project: this recently granted project is focused on investigating options for flexibility in local electricity grids by using vehicle-to-grid technology in neighborhoods in Utrecht and Arnhem. We require experience with data analysis and simulation using Python. In this project we collaborate with WeDriveSolar and TUDelft. We have funding for at least 44 months available. 2) PVObs and SolFaSi projects. Both projects deal with photovoltaic performance and forecasting. The PVObs project aims to create a large database of PV systems in the Netherlands in order to assess their performance and from that determine with better accuracy the contribution of PV to the national electricity demand. Parnters in this project are CBS and SunData. The SolFaSi project aims to develop PV forecasting at high time and spatial resolutions using all-sky imagers (aka cloud cameras). We will install three cameras at the UU campus and one at KNMI. This network allows to construct a moving shadow field over the campus. This should be correlated with the power generation of the 4000+ PV panels at campus buildings. We require experience with (big) data analysis and simulation using Python. We have funding for 24 months available. If you are interested, or if you know someone who may be interested, please send me a mail at w.g.j.h....(a)uu.nl best regards Wilfried [View Less]

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NumPy Tutorials
by Melissa Mendonça April 30, 2021

April 30, 2021

Hello, all! Recently, we added a link to NumPy Tutorials to our main page ( https://numpy.org/numpy-tutorials) This set of tutorials has been built with the collaboration of a few contributors, who have put effort and dedication into them. Some of this content was generated as part of last year's Google Season of Docs program, with authors kubedoc and Ryan Cooper. These tutorials are meant to be not only about NumPy, but about using NumPy to solve concrete problems, which means more … [View More]

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