Mailman 3 February 2015 - 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 do I avoid these messages to appear? I know that I could in principle use numpy.seterr. However, I do NOT want to remove these warnings for other potential divide/multiply/sqrt etc errors. Only when I am using a "where", to in fact avoid such warnings! Note that the warnings only happen once, but since I am going to release that code, I would like to avoid the user to get such messages which are irrelevant here (because I am testing, with the where, when NOT to divide by zero or take a sqrt of a negative number). thanks! Eric

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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 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

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C-API: multidimensional array indexing?
by Johann Bauer March 31, 2016

March 31, 2016

Dear experts, is there a C-API function for numpy which implements Python's multidimensional indexing? Say, I have a 2d-array PyArrayObject * M; and an index int i; how do I extract the i-th row or column M[i,:] respectively M[:,i]? I am looking for a function which gives again a PyArrayObject * and which is a view to M (no copied data; the result should be another PyArrayObject whose data and strides points to the correct memory portion of M). I searched the API documentation, Google and mailing lists for quite a long time but didn't find anything. Can you help me? Thanks, Johann

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Generalize hstack/vstack --> stack; Block matrices like in matlab
by Stefan Otte Jan. 9, 2016

Jan. 9, 2016

Hey, quite often I work with block matrices. Matlab offers the convenient notation [ a b; c d ] to stack matrices. The numpy equivalent is kinda clumsy: vstack([hstack([a,b]), hstack([c,d])]) I wrote the little function `stack` that does exactly that: stack([[a, b], [c, d]]) In my case `stack` replaced `hstack` and `vstack` almost completely. If you're interested in including it in numpy I created a pull request [1]. I'm looking forward to getting some feedback! Best, Stefan [1] https://github.com/numpy/numpy/pull/5057

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F2PY and multi-dimension arrays - unexpected array size error
by Backing Olof July 1, 2015

July 1, 2015

Hi I am helping out with a Python and Fortran project. Let me give you some background: * Fortran source: C Bergstrom FCC C User subroutine VUMAT subroutine VUMAT( C Read only - * nblock, ndir, nshr, nstatev, nprops, * stepTime, dt, * props, * density, strainInc, * tempOld, * stressOld, stateOld, enerInternOld, enerInelasOld, C Write only - * stressNew, stateNew, enerInternNew, enerInelasNew ) C include 'vaba_param.inc' C dimension props(nprops), 1 density(nblock), strainInc(nblock,ndir+nshr), 2 tempOld(nblock), 5 stressOld(nblock,ndir+nshr), stateOld(nblock,nstatev), 6 enerInternOld(nblock), enerInelasOld(nblock), 2 stressNew(nblock,ndir+nshr), stateNew(nblock,nstatev), 3 enerInternNew(nblock), enerInelasNew(nblock) * Corresponding .pyf integer :: nblock integer :: ndir integer :: nshr integer :: nstatev integer :: nprops real :: steptime real :: dt real dimension(nprops) :: props real dimension(nblock) :: density real dimension(nblock,ndir+nshr) :: straininc real dimension(nblock) :: tempold real dimension(nblock,ndir+nshr) :: stressold real dimension(nblock,nstatev) :: stateold real dimension(nblock) :: enerinternold real dimension(nblock) :: enerinelasold real dimension(nblock,ndir+nshr),intent(out) :: stressnew real dimension(nblock,nstatev),intent(out) :: statenew real dimension(nblock),intent(out) :: enerinternnew real dimension(nblock),intent(out) :: enerinelasnew * Python source with call of Fortran routine: nblock = 1 ndir = 3 nshr = 3 nstatev = 3 nprops = 11 stepTime = 1 dt = 1 props = np.array([10, 0.5, 1e10, 5, 1e12, 3e-6, 8e-6, 27, 2], float) density = np.array([[7.8e3]], float) strainInc = np.array([[1,-0.5,-0.5,0,0,0]], float) tempOld = np.array([[1]], float) stressOld = np.array([[1,1,1,1,1,1]], float) stateOld = np.array([[1,1,1]], float) enerInternOld = np.array([1], float) enerInelasOld = np.array([1], float) stressNew = np.array([[]], float) stateNew = np.array([[]], float) enerInternNew = np.array([[]], float) enerInelasNew = np.array([[]], float) stressNew, stateNew, enerInternNew, enerInelasNew = vumat(nblock, ndir, nshr, nstatev, nprops, stepTime, dt, props, density, strainInc, tempOld, stressOld, stateOld, enerInternOld, enerInelasOld) When trying to run with Python 2.7 I get: olof@ubuntu:~$ ./demo.py unexpected array size: new_size=4, got array with arr_size=1 Traceback (most recent call last): File "./demo.py", line 33, in <module> main() File "./demo.py", line 30, in main stressNew, stateNew, enerInternNew, enerInelasNew = vumat(nblock, ndir, nshr, nstatev, nprops, stepTime, dt, props, density, strainInc, tempOld, stressOld, stateOld, enerInternOld, enerInelasOld) VUMAT_Bergstrom_FCC.error: failed in converting 9th argument `stressold' of VUMAT_Bergstrom_FCC.vumat to C/Fortran array Other stuff: * python 2.7.6 * numpy/f2py 1.8.2 * gcc/gfortran 4.8.2 * ubuntu 14.04 LTS 32-bit I have tried to google, read the f2py manual, fortran tutorials etc, but to no avail. I must also admit that my knowledge in python is so-so and fortran even less(!). What is the missing statement/syntax that I can’t get correct? Your humble programmer, Olof

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PR added: frozen dimensions in gufunc signatures
by Jaime Fernández del Río June 23, 2015

June 23, 2015

Hi, I have just sent a PR (https://github.com/numpy/numpy/pull/5015), adding the possibility of having frozen dimensions in gufunc signatures. As a proof of concept, I have added a `cross1d` gufunc to `numpy.core.umath_tests`: In [1]: import numpy as np In [2]: from numpy.core.umath_tests import cross1d In [3]: cross1d.signature Out[3]: '(3),(3)->(3)' In [4]: a = np.random.rand(1000, 3) In [5]: b = np.random.rand(1000, 3) In [6]: np.allclose(np.cross(a, b), cross1d(a, b)) Out[6]: True In [7]: %timeit np.cross(a, b) 10000 loops, best of 3: 76.1 us per loop In [8]: %timeit cross1d(a, b) 100000 loops, best of 3: 13.1 us per loop In [9]: c = np.random.rand(1000, 2) In [10]: d = np.random.rand(1000, 2) In [11]: cross1d(c, d) --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-11-72c66212e40c> in <module>() ----> 1 cross1d(c, d) ValueError: cross1d: Operand 0 has a mismatch in its core dimension 0, with gufunc signature (3),(3)->(3) (size 2 is different from 3) The speed up over `np.cross` is nice, and while `np.cross` is not the best of examples, as it needs to handle more sizes, in many cases this will allow producing gufuncs that work without a Python wrapper redoing checks that are best left to the iterator, such as dimension sizes. It still needs tests, but before embarking on fully developing those, I wanted to make sure that there is an interest on this. I would also like to further enhance gufuncs providing computed dimensions, e.g. making it possible to e.g. define `pairwise_cross` with signature '(n, 3)->($m, 3)', where the $ indicates that m is a computed dimension, that would have to be calculated by a function passed to the gufunc constructor and stored in the gufunc object, based on the other core dimensions. In this case it would make $m be n*(n-1), so that all pairwise cross products between 3D vectors could be computed. The syntax with '$' is kind of crappy, so any suggestions on how to better express this in the signature are more than welcome, as well as any feedback on the merits (or lack of them) of implementing this. Jaime -- (\__/) ( O.o) ( > <) Este es Conejo. Copia a Conejo en tu firma y ayúdale en sus planes de dominación mundial.

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Problem with _dotblas.pyd when using Matplotlib for 3d plot
by Geoffrey Mégardon March 19, 2015

March 19, 2015

Hi, I am on Windows 8.1, 64-bit, using the usual version of Anaconda-64-bit for windows. I just come from the mailing list of matplotlib: my PC is not able to do 3D plot with matplotlib. Python crashes completely if I try to plot something in 3D and to show it: "Python.exe has stopped working..." The main problem in resolving my issue is that the python traceback and the faulthandler package do not succeed in catching the errors. Only the Windows crash report allows us to see what happened before the crash. The windows report tells that the file _dotblas.pyd from numpy is likely responsible for the crash. So I tried a simple: import numpy; numpy.test() numpy.test() does not work! I dont get any traceback from python. It just crashes as before: "Python.exe has stopped working..." And again, the Windows report blames _dotblas.pyd :) Here the new Windows report for the numpy.test(), I highlighted the lines which blame _dotblas.pyd: Version=1 EventType=APPCRASH EventTime=130608138227608275 ReportType=2 Consent=1 UploadTime=130608138229728384 ReportIdentifier=216b16d2-6f5c-11e4-bec3-48d22435da2b IntegratorReportIdentifier=216b16d1-6f5c-11e4-bec3-48d22435da2b NsAppName=python.exe Response.BucketId=398b7eee350a0fd8a7a96f705d3488f6 Response.BucketTable=4 Response.LegacyBucketId=85979911964 Response.type=4 Sig[0].Name=Application Name Sig[0].Value=python.exe Sig[1].Name=Application Version Sig[1].Value=0.0.0.0 Sig[2].Name=Application Timestamp Sig[2].Value=53b4679e *Sig[3].Name=Fault Module NameSig[3].Value=_dotblas.pyd* Sig[4].Name=Fault Module Version Sig[4].Value=0.0.0.0 Sig[5].Name=Fault Module Timestamp Sig[5].Value=545678cb Sig[6].Name=Exception Code Sig[6].Value=c000001d Sig[7].Name=Exception Offset Sig[7].Value=0000000000324022 DynamicSig[1].Name=OS Version DynamicSig[1].Value=6.3.9600.2.0.0.768.101 DynamicSig[2].Name=Locale ID DynamicSig[2].Value=2057 DynamicSig[22].Name=Additional Information 1 DynamicSig[22].Value=f32f DynamicSig[23].Name=Additional Information 2 DynamicSig[23].Value=f32feb95f950f918532aa47b4372840e DynamicSig[24].Name=Additional Information 3 DynamicSig[24].Value=9882 DynamicSig[25].Name=Additional Information 4 DynamicSig[25].Value=98823b6c7f579b24e92112ab827fe4a1 UI[2]=C:\Anaconda\python.exe UI[3]=python.exe has stopped working UI[4]=Windows can check online for a solution to the problem. UI[5]=Check online for a solution and close the program UI[6]=Check online for a solution later and close the program UI[7]=Close the program LoadedModule[0]=C:\Anaconda\python.exe LoadedModule[1]=C:\WINDOWS\SYSTEM32\ntdll.dll LoadedModule[2]=C:\WINDOWS\system32\KERNEL32.DLL LoadedModule[3]=C:\WINDOWS\system32\KERNELBASE.dll LoadedModule[4]=C:\Anaconda\python27.dll LoadedModule[5]=C:\WINDOWS\WinSxS\amd64_microsoft.vc90.crt_1fc8b3b9a1e18e3b_9.0.30729.8387_none_08e793bfa83a89b5\MSVCR90.dll LoadedModule[6]=C:\WINDOWS\system32\USER32.dll LoadedModule[7]=C:\WINDOWS\system32\ADVAPI32.dll LoadedModule[8]=C:\WINDOWS\system32\SHELL32.dll LoadedModule[9]=C:\WINDOWS\system32\GDI32.dll LoadedModule[10]=C:\WINDOWS\system32\msvcrt.dll LoadedModule[11]=C:\WINDOWS\SYSTEM32\sechost.dll LoadedModule[12]=C:\WINDOWS\system32\RPCRT4.dll LoadedModule[13]=C:\WINDOWS\SYSTEM32\combase.dll LoadedModule[14]=C:\WINDOWS\system32\SHLWAPI.dll LoadedModule[15]=C:\WINDOWS\system32\IMM32.DLL LoadedModule[16]=C:\WINDOWS\system32\MSCTF.dll LoadedModule[17]=C:\Anaconda\DLLs\_socket.pyd LoadedModule[18]=C:\WINDOWS\system32\WS2_32.dll LoadedModule[19]=C:\WINDOWS\system32\NSI.dll LoadedModule[20]=C:\Anaconda\DLLs\_ssl.pyd LoadedModule[21]=C:\Anaconda\DLLs\_ctypes.pyd LoadedModule[22]=C:\WINDOWS\system32\ole32.dll LoadedModule[23]=C:\WINDOWS\system32\OLEAUT32.dll LoadedModule[24]=C:\Anaconda\DLLs\_hashlib.pyd LoadedModule[25]=C:\WINDOWS\SYSTEM32\CRYPTSP.dll LoadedModule[26]=C:\WINDOWS\system32\rsaenh.dll LoadedModule[27]=C:\WINDOWS\SYSTEM32\bcrypt.dll LoadedModule[28]=C:\WINDOWS\SYSTEM32\CRYPTBASE.dll LoadedModule[29]=C:\WINDOWS\SYSTEM32\bcryptPrimitives.dll LoadedModule[30]=C:\Users\User\AppData\Roaming\Python-Eggs\faulthandler-2.4-py2.7-win-amd64.egg-tmp\faulthandler.pyd LoadedModule[31]=C:\Anaconda\lib\site-packages\numpy\core\multiarray.pyd LoadedModule[32]=C:\Anaconda\lib\site-packages\numpy\core\umath.pyd LoadedModule[33]=C:\Anaconda\lib\site-packages\numpy\core\_dotblas.pyd LoadedModule[34]=C:\Anaconda\lib\site-packages\numpy\core\libiomp5md.dll LoadedModule[35]=C:\Anaconda\lib\site-packages\numpy\core\scalarmath.pyd LoadedModule[36]=C:\Anaconda\lib\site-packages\numpy\lib\_compiled_base.pyd LoadedModule[37]=C:\Anaconda\lib\site-packages\numpy\linalg\lapack_lite.pyd LoadedModule[38]=C:\Anaconda\lib\site-packages\numpy\linalg\_umath_linalg.pyd LoadedModule[39]=C:\Anaconda\lib\site-packages\numpy\fft\fftpack_lite.pyd LoadedModule[40]=C:\Anaconda\lib\site-packages\numpy\random\mtrand.pyd LoadedModule[41]=C:\Anaconda\DLLs\_multiprocessing.pyd State[0].Key=Transport.DoneStage1 State[0].Value=1 FriendlyEventName=Stopped working ConsentKey=APPCRASH AppName=python.exe AppPath=C:\Anaconda\python.exe NsPartner=windows NsGroup=windows8 ApplicationIdentity=5B036AF1EC2E20F320DBF28D119DE93D Any idea, of what I can do? I try to reinstall anaconda, I also try conda remove numpy and conda install numpy. That does not seem to change anything. -- -- MEGARDON Geoffrey

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Would like to patch docstring for numpy.random.normal
by Daniel Sank March 10, 2015

March 10, 2015

Dear numpy users, I would like to clarify the docstring for numpy.random.normal. I submitted a patch but it was rejected because it breaks the tests. Unfortunately, the development workflow page does not explain how to run the tests (in fact it doesn't mention them at all). I am therefore writing to discuss my proposed change and find out how to run the tests so that I can make it compatible with the existing code. The current form of the docstring for numpy.random.normal is as follows: """ normal(loc=0.0, scale=1.0, size=None) <snip> Parameters ---------- loc : float Mean ("centre") of the distribution. scale : float Standard deviation (spread or "width") of the distribution. size : tuple of ints Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. <snip> Notes ----- The probability density for the Gaussian distribution is .. math:: p(x) = \frac{1}{\sqrt{ 2 \pi \sigma^2 }} e^{ - \frac{ (x - \mu)^2 } {2 \sigma^2} }, where :math:`\mu` is the mean and :math:`\sigma` the standard deviation. The square of the standard deviation, :math:`\sigma^2`, is called the variance. """ It seems unnecessarily convoluted to name the input arguments "loc" and "scale", then immediately define them as the "mean" and "standard deviation" in the Parameters section, and then again rename them as "mu" and "sigma" in the written formula. I propose to simply change the argument names to "mean" and "sigma" to improve consistency. I tried fixing this via a pull request [1] but it was closed because my change broke the tests. Unfortunately, the development workflow section of the web page doesn't explain how to run the tests (in fact it doesn't even mention them). How can I make the proposed change without breaking the tests, or equivalently how do I find out how to run the tests myself so I can find an acceptable way of making the change on my own? [1] https://github.com/numpy/numpy/pull/5607#issuecomment-76114282 -- Daniel Sank

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dot and MKL memory
by Mads Ipsen Feb. 27, 2015

Feb. 27, 2015

Hi, If I build Python 2.7.2 and numpy-1.9.1 and run the following script import numpy c = numpy.ones((500000, 4)) mat = numpy.identity(4) r = numpy.dot(c, mat) the evaluation of the 'dot' increases the memory by app. 35 MB. If, in addition, I build numpy-1.9.1 with MKL support, and run the script, the evaluation of the 'dot' increases the memory by app. 450 MB. Is the expected? Best regards, Mads specs: Ubuntu 12.04 ifort (IFORT) 14.0.1 2013100 gcc version 4.6.3 (Ubuntu/Linaro 4.6.3-1ubuntu5) -- +---------------------------------------------------------------------+ | Mads Ipsen | +----------------------------------+----------------------------------+ | Overgaden Oven Vandet 106, 4.tv | phone: +45-29716388 | | DK-1415 København K | email: mads.ipsen(a)gmail.com | | Denmark | map : https://goo.gl/maps/oQ6y6 | +----------------------------------+----------------------------------+

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