Mailman 3 August 2013 - NumPy-Discussion

Fwd: New release of bdist_mpkg
by Matthew Brett Aug. 26, 2013

Aug. 26, 2013

Hi, Just in case y'all didn't catch this from other lists: I just did a new release of bdist_mpkg: https://pypi.python.org/pypi/bdist_mpkg/ The main new feature is Python 3 compatibility: https://github.com/matthew-brett/bdist_mpkg/blob/master/Changelog Thanks to Bob Ippolito for the original code and Ronald Oussoren for taking care of it. Please do let me know if you hit any bugs or misfeatures, Cheers, Matthew

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numpy dot returns [nan nan nan]
by Tom Bennett Aug. 26, 2013

Aug. 26, 2013

Hi All, I have two arrays, A and B.A is 3 x 100,000 and B is 100,000. If I do np.dot(A,B), I get [nan, nan, nan]. However, np.any(np.isnan(A))==False and np.any(no.isnan(B))==False. And also np.seterr(all='print') does not print anything. I am not wondering what is going on and how to avoid. In case it is important, A and B are from the normal equation of doing regression. I am regressing 100,000 observations on 3 100,000 long factors. Thanks, Tom

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1.8.0 branch reminder
by Charles R Harris Aug. 26, 2013

Aug. 26, 2013

Just a reminder that 1.8.0 will be branched tonight. I've put up a big STY: PR <https://github.com/numpy/numpy/pull/3635> that removes trailing whitespace and fixes spacing after commas. I would like to apply before the branch, but it may cause merge difficulties down the line. I'd like feedback on that option. Thoughts? Chuck

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OS X binaries for releases
by Ralf Gommers Aug. 26, 2013

Aug. 26, 2013

Hi all, Building binaries for releases is currently quite complex and time-consuming. For OS X we need two different machines, because we still provide binaries for OS X 10.5 and PPC machines. I propose to not do this anymore. It doesn't mean we completely drop support for 10.5 and PPC, just that we don't produce binaries. PPC was phased out in 2006 and OS X 10.6 came out in 2009, so there can't be a lot of demand for it (and the download stats at http://sourceforge.net/projects/numpy/files/NumPy/1.7.1/confirm this). Furthermore I propose to not provide 2.6 binaries anymore. Downloads of 2.6 OS X binaries were <5% of the 2.7 ones. We did the same with 2.4 for a long time - support it but no binaries. So what we'd have left at the moment is only the 64-bit/32-bit universal binary for 10.6 and up. What we finally need to add is 3.x OS X binaries. We can make an attempt to build these on 10.8 - since we have access to a hosted 10.8 Mac Mini it would allow all devs to easily do a release (leaving aside the Windows issue). If anyone has tried the 10.6 SDK on 10.8 and knows if it actually works, that would be helpful. Any concerns, objections? Cheers, Ralf P.S. the same proposal applies of course also to scipy

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Strange behavior with boolean slices...
by Cera, Tim Aug. 26, 2013

Aug. 26, 2013

I have done this before, but am now really confused. Created an array 'day' specifying the 'f' type In [29]: day Out[29]: array([ 5., 5.], dtype=float32) # Have a mask... In [30]: mask Out[30]: array([ True, False], dtype=bool) # So far, so good... In [31]: day[mask] Out[31]: array([ 5.], dtype=float32) In [32]: day[mask] = 10 # What? In [33]: day Out[33]: array([ 10., 10.], dtype=float32) So I created an integer array 'a' In [38]: a Out[38]: array([11, 1]) In [39]: a[mask] Out[39]: array([11]) In [40]: a[mask] = 12 # This is what I expect. In [41]: a Out[41]: array([12, 1]) Am I missing something? Is this supposed to happen? Version 1.7.1. Kindest regards, Tim

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RAM problem during code execution - Numpya arrays
by Josè Luis Mietta Aug. 23, 2013

Aug. 23, 2013

Hi ecperts. I need your help with a RAM porblem during execution of my script. I wrote the next code. I use SAGE. In 1-2 hours of execution time the RAM of my laptop (8gb) is filled and the sistem crash: fromscipy.stats importuniform importnumpy asnp cant_de_cadenas =[700,800,900]cantidad_de_cadenas=np.array([])forkkkkk incant_de_cadenas:cantidad_de_cadenas=np.append(cantidad_de_cadenas,kkkkk)cantidad_de_cadenas=np.transpose(cantidad_de_cadenas)b=10h=b Longitud=1numero_experimentos=150densidad_de_cadenas =cantidad_de_cadenas/(b**2)prob_perc=np.array([])tiempos=np.array([])S_int=np.array([])S_medio=np.array([])desviacion_standard=np.array([])desviacion_standard_nuevo=np.array([])anisotropia_macroscopica_porcentual=np.array([])componente_y=np.array([])componente_x=np.array([])importtime forN incant_de_cadenas:empieza=time.clock()PERCOLACION=np.array([])size_medio_intuitivo =np.array([])size_medio_nuevo =np.array([])std_dev_size_medio_intuitivo =np.array([])std_dev_size_medio_nuevo =np.array([])comp_y =np.array([])comp_x =np.array([])foru inxrange(numero_experimentos):perco =Falsearray_x1=uniform.rvs(loc=-b/2,scale=b,size=N)array_y1=uniform.rvs(loc=-h/2,scale=h,size=N)array_angle=uniform.rvs(loc=-0.5*(np.pi),scale=np.pi,size=N)array_pendiente_x=1./np.tan(array_angle)random=uniform.rvs(loc=-1,scale=2,size=N)lambda_sign=np.zeros([N])fort inxrange(N):ifrandom[t]<0:lambda_sign[t]=-1else:lambda_sign[t]=1array_lambdas=(lambda_sign*Longitud)/np.sqrt(1+array_pendiente_x**2)array_x2=array_x1 +array_lambdas*array_pendiente_x array_y2=array_y1 +array_lambdas*1array_x1 =np.append(array_x1,[-b/2,b/2,-b/2,-b/2])array_y1 =np.append(array_y1,[-h/2,-h/2,-h/2,h/2])array_x2 =np.append(array_x2,[-b/2,b/2,b/2,b/2])array_y2 =np.append(array_y2,[h/2,h/2,-h/2,h/2])M =np.zeros([N+4,N+4])forj inxrange(N+4):ifj>0:x_A1B1 =array_x2[j]-array_x1[j]y_A1B1 =array_y2[j]-array_y1[j]x_A1A2 =array_x1[0:j]-array_x1[j]y_A1A2 =array_y1[0:j]-array_y1[j]x_A2A1 =-1*x_A1A2 y_A2A1 =-1*y_A1A2 x_A2B2 =array_x2[0:j]-array_x1[0:j]y_A2B2 =array_y2[0:j]-array_y1[0:j]x_A1B2 =array_x2[0:j]-array_x1[j]y_A1B2 =array_y2[0:j]-array_y1[j]x_A2B1 =array_x2[j]-array_x1[0:j]y_A2B1 =array_y2[j]-array_y1[0:j]p1 =x_A1B1*y_A1A2 -y_A1B1*x_A1A2 p2 =x_A1B1*y_A1B2 -y_A1B1*x_A1B2 p3 =x_A2B2*y_A2B1 -y_A2B2*x_A2B1 p4 =x_A2B2*y_A2A1 -y_A2B2*x_A2A1 condicion_1=p1*p2 condicion_2=p3*p4 fork inxrange (j):ifcondicion_1[k]<=0andcondicion_2[k]<=0:M[j,k]=1delcondicion_1 delcondicion_2 ifj+1<N+4:x_A1B1 =array_x2[j]-array_x1[j]y_A1B1 =array_y2[j]-array_y1[j]x_A1A2 =array_x1[j+1:]-array_x1[j]y_A1A2 =array_y1[j+1:]-array_y1[j]x_A2A1 =-1*x_A1A2 y_A2A1 =-1*y_A1A2 x_A2B2 =array_x2[j+1:]-array_x1[j+1:]y_A2B2 =array_y2[j+1:]-array_y1[j+1:]x_A1B2 =array_x2[j+1:]-array_x1[j]y_A1B2 =array_y2[j+1:]-array_y1[j]x_A2B1 =array_x2[j]-array_x1[j+1:]y_A2B1 =array_y2[j]-array_y1[j+1:]p1 =x_A1B1*y_A1A2 -y_A1B1*x_A1A2 p2 =x_A1B1*y_A1B2 -y_A1B1*x_A1B2 p3 =x_A2B2*y_A2B1 -y_A2B2*x_A2B1 p4 =x_A2B2*y_A2A1 -y_A2B2*x_A2A1 condicion_1=p1*p2 condicion_2=p3*p4 fork inxrange ((N+4)-j-1):ifcondicion_1[k]<=0andcondicion_2[k]<=0:M[j,k+j+1]=1delcondicion_1 delcondicion_2 M[N,N+2]=0M[N,N+3]=0M[N+1,N+2]=0M[N+1,N+3]=0M[N+2,N]=0M[N+2,N+1]=0M[N+3,N]=0M[N+3,N+1]=0CD=np.array([])POPOPO=[]forg inxrange(N):lala=0r=Falsewhilelala<=len(POPOPO)-1:esta=g inPOPOPO[lala]ifesta isTrue:lala=len(POPOPO)r=Trueelse:lala=lala+1ifr isFalse:L=np.array([g])fors inxrange(N):ifM[g,s]!=0:L=np.append(L,s)x=0whilex<=N:forl inxrange(N):z=l inL d=L[x]ifz isFalseandM[d,l]!=0:L=np.append(L,l)ifx+1<len(L):x+=1else:x=N+1.q=len (L)CD=np.append(CD,q)POPOPO.append(L)M_horizontal=M.copy()M_horizontal[:,N+2]=np.zeros(N+4)M_horizontal[:,N+3]=np.zeros(N+4)M_horizontal[N+2]=np.zeros(N+4)M_horizontal[N+3]=np.zeros(N+4)L=np.array([N])fors inxrange(N+4):ifM_horizontal[N,s]!=0:L=np.append(L,s)x=0whilex<=N+4:forl inxrange(N+4):z=l inL d=L[x]ifz isFalseandM_horizontal[d,l]!=0:L=np.append(L,l)ifx+1<len(L):x+=1else:x=(N+4)+1.LV1_in_L =N inL LV2_in_L=(N+1)inL ifLV1_in_L isTrueandLV2_in_L isTrue:perc_horiz=Trueelse:perc_horiz=FalseM_vertical=M.copy()M_vertical[:,N]=np.zeros(N+4)M_vertical[:,N+1]=np.zeros(N+4)M_vertical[N]=np.zeros(N+4)M_vertical[N+1]=np.zeros(N+4)L=np.array([N+2])fors inxrange(N+4):ifM_vertical[N+2,s]!=0:L=np.append(L,s)x=0whilex<=N+4:forl inxrange(N+4):z=l inL d=L[x]ifz isFalseandM_vertical[d,l]!=0:L=np.append(L,l)ifx+1<len(L):x+=1else:x=(N+4)+1.LH1_in_L =(N+2)inL LH2_in_L=(N+3)inL ifLH1_in_L isTrueandLH2_in_L isTrue:perc_ver =Trueelse:perc_ver =Falseifperc_ver isTrueorperc_horiz isTrue:PERCOLACION=np.append(PERCOLACION,1)perco=TrueD =np.array([])W =np.array([])forc inxrange (int(min(CD)),int(max(CD)+1),1):D=np.append(D,c)frec =sum (CD ==c)W =np.append(W,frec)ifperco isTrue:posicion=np.argmax(D)D=np.delete(D,posicion)W=np.delete(W,posicion)iflen(D)==0andlen(W)==0:S_medio_intuitivo_exp_u=0S_medio_nuevo_exp_u =0std_dev_exp_u =0std_dev_nuevo_exp_u =0else:S_medio_intuitivo_exp_u =np.average (D,weights=W)peso_nuevo=D*W S_medio_nuevo_exp_u =np.average (D,weights=peso_nuevo)tipos=sum(W)X=W*((D-S_medio_intuitivo_exp_u)**2)S=sum(X)std_dev_exp_u =np.sqrt(S/(tipos-1.))tipos_nuevo=sum(peso_nuevo)X_nuevo=peso_nuevo*((D-S_medio_nuevo_exp_u)**2)S_nuevo=sum(X_nuevo)std_dev_nuevo_exp_u =np.sqrt(S_nuevo/(tipos_nuevo-1.))componente_longitudinal=Longitud*np.abs(np.cos(array_angle))comp_y=np.append(comp_y,sum(componente_longitudinal)/N)componente_transversal=Longitud*np.abs(np.sin(array_angle))comp_x=np.append(comp_x,sum(componente_transversal)/N)std_dev_size_medio_intuitivo=np.append(std_dev_size_medio_intuitivo,std_dev_exp_u)std_dev_size_medio_nuevo=np.append(std_dev_size_medio_nuevo,std_dev_nuevo_exp_u)size_medio_intuitivo=np.append(size_medio_intuitivo,S_medio_intuitivo_exp_u)size_medio_nuevo=np.append(size_medio_nuevo,S_medio_nuevo_exp_u)percolation_probability=sum(PERCOLACION)/numero_experimentos prob_perc=np.append(prob_perc,percolation_probability)S_int =np.append (S_int,sum(size_medio_intuitivo)/numero_experimentos)S_medio=np.append (S_medio,sum(size_medio_nuevo)/numero_experimentos)desviacion_standard =np.append (desviacion_standard,sum(std_dev_size_medio_intuitivo)/numero_experimentos)desviacion_standard_nuevo=np.append (desviacion_standard_nuevo,sum(std_dev_size_medio_nuevo)/numero_experimentos)tiempos=np.append(tiempos,time.clock()-empieza)componente_y=np.append(componente_y,sum(comp_y)/numero_experimentos)componente_x=np.append(componente_x,sum(comp_x)/numero_experimentos)anisotropia_macroscopica_porcentual=100*(1-(componente_y/componente_x)) I tryed with gc and gc.collect() and 'del'command for deleting arrays after his use and nothing work! What am I doing wrong? Why the memory becomes full while running (starts with 10% of RAM used and in 1-2hour is totally full used)? Please help me, I'm totally stuck! Thanks a lot!

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What is a numpy.long type?
by Chris Barker - NOAA Federal Aug. 23, 2013

Aug. 23, 2013

Hi folks, I had thought that maybe a numpy.long dtype was a system (compiler)-native C long. But on both 32 and 64 bit python on OS-X, it seems to be 64 bit. I'm pretty sure that on OS-X 32 bit, a C long is 32 bits. (gdd, of course) I don't have other machines to test on , but as the MS compilers and gcc do different things with a C long on 64 bit platforms, I"m curious how numpy defines it. In general, I prefer the "explicit is better than implicit" approach and use, e.g. int32 and int64. However, in this case, we are using Cython, and calling C code that used "long" -- so what I want is "whatever the compiler thinks is a long" -- is there a way to do that without my own kludgy platform-dependent code? I note that the Cython numpy.pxd assigns: ctypedef signed long npy_long Is that a bug? (or maybe npy_long is not supposed to match np.long.... -Chris -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/OR&R (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception Chris.Barker(a)noaa.gov

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Warnings not raised by np.log in 32 bit build on Windows
by Warren Weckesser Aug. 23, 2013

Aug. 23, 2013

I'm investigating a test error in scipy 0.13.0 beta 1 that was reported by Christoph Gohlke. The scipy issue is here: https://github.com/scipy/scipy/issues/2771 I don't have a Windows environment to test it myself, but Christoph reported that this code: ``` import numpy as np data = np.array([-0.375, -0.25, 0.0]) s = np.log(data) ``` does not generate two RuntimeWarnings when it is run with numpy 1.7.1 in a 32 bit Windows 8 environment (numpy 1.7.1 compiled with Visual Studio compilers and Intel's MKL). In 64 bit Windows, and in 64 bit linux, it generates two RuntimeWarnings. The inconsistency seems like a bug, possibly this one: https://github.com/numpy/numpy/issues/1958. Can anyone check if this also occurs in the development branch? Warren

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Numpy/Scipy sprint this Sunday
by Ralf Gommers Aug. 22, 2013

Aug. 22, 2013

Hi all, Here's a reminder that we have a sprint this Sunday (Aug 25) at EuroScipy. There are already 12 people who indicated they'd join; even more would be even better. Location: https://www.euroscipy.org/venue/ (room K3.601) Time: 9am - 7pm More details: https://github.com/rgommers/scipy/wiki/EuroSciPy%2713-numpy-scipy-sprint No previous experience contributing to numpy and scipy required (although you should know how to use python/numpy/scipy/git to a reasonable extent). Please try to get your development environment set up beforehand - see developer guides at http://docs.scipy.org/doc/ for explanation of how to do that. If you can't join in Brussels, we'll also be on IRC and mailing lists. Hope to see a large turnout! Cheers, Ralf

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ANN: Spyder v2.2.3 released
by Pierre Raybaut Aug. 21, 2013

Aug. 21, 2013

Hi all, On the behalf of Spyder's development team ( http://code.google.com/p/spyderlib/people/list), I'm pleased to announce that Spyder v2.2.3 has been released and is available for Windows XP/Vista/7/8, GNU/Linux and MacOS X: http://code.google.com/p/spyderlib/. This is a maintenance release of the v2.2 branch which is the last release to support Python 2.5: * Spyder 2.2 supports Python 2.5 to 2.7 * Spyder 2.3 will support Python 2.7 to 3.3 * (Spyder 2.3.0dev6 is also available as of today: this is an experimental release but quite stable which already supports Python 3) See also https://code.google.com/p/spyderlib/downloads/list. Since v2.2.2: * Several bug have been fixed (see changelog for further details: https://code.google.com/p/spyderlib/wiki/ChangeLog) * New features have been added like full support for a MATLAB-like cell mode (see "Run" menu) and the Optional Dependencies dialog box (see menu "?") which gives the user a status of Spyder's dependencies Spyder is a free, open-source (MIT license) interactive development environment for the Python language with advanced editing, interactive testing, debugging and introspection features. Originally designed to provide MATLAB-like features (integrated help, interactive console, variable explorer with GUI-based editors for dictionaries, NumPy arrays, ...), it is strongly oriented towards scientific computing and software development. Thanks to the `spyderlib` library, Spyder also provides powerful ready-to-use widgets: embedded Python console (example: http://packages.python.org/guiqwt/_images/sift3.png), NumPy array editor (example: http://packages.python.org/guiqwt/_images/sift2.png), dictionary editor, source code editor, etc. Description of key features with tasty screenshots can be found at: http://code.google.com/p/spyderlib/wiki/Features Don't forget to follow Spyder updates/news: * on the project website: http://code.google.com/p/spyderlib/ * and on our official blog: http://spyder-ide.blogspot.com/ Last, but not least, we welcome any contribution that helps making Spyder an efficient scientific development/computing environment. Join us to help creating your favourite environment! (http://code.google.com/p/spyderlib/wiki/NoteForContributors) Enjoy! -Pierre

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