Mailman 3 April 2011 - NumPy-Discussion

ATLAS - support letter
by Matthew Brett 21 Apr '11

21 Apr '11

Hi, I'm on the ATLAS mailing list, maybe some of y'all are too. Clint Whaley, the author of ATLAS, was asking for letters to support his tenure case. That is, letters saying that lots of us benefit greatly from his work - which is obviously true. Can we the numpy community produce such a letter? Who would it best come from? Cheers, Matthew

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Suggestion: Add the unsqueeze function to numpy
by Yannick Copin 20 Apr '11

20 Apr '11

Hi, I'm a very frequent user of the following "unsqueeze" function, which I initially copied from scipy/stats/models/robust/scale.py and which seems to be also present in scikits.statsmodels.tools.unsqueeze. Would it be possible to include it natively to numpy? def unsqueeze(data, axis, oldshape): """ Unsqueeze a collapsed array >>> from numpy import mean >>> from numpy.random import standard_normal >>> x = standard_normal((3,4,5)) >>> m = mean(x, axis=1) >>> m.shape (3, 5) >>> m = unsqueeze(m, 1, x.shape) >>> m.shape (3, 1, 5) """ if axis is None: return data newshape = list(oldshape) newshape[axis] = 1 return data.reshape(newshape) Cheers, -- .~. Yannick COPIN (o:>* Doctus cum libro /V\ Institut de physique nucléaire de Lyon // \\ Université de Lyon - CNRS-IN2P3 /( )\ AIM: YnCopin ICQ: 236931013 ^`~'^ http://snovae.in2p3.fr/ycopin/

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Beginner's question
by Laszlo Nagy 20 Apr '11

20 Apr '11

Hi All, I have this example program: import numpy as np import numpy.random as rnd def dim_weight(X): weights = X[0] volumes = X[1]*X[2]*X[3] res = np.empty(len(volumes), dtype=np.double) for i,v in enumerate(volumes): if v>5184: res[i] = v/194.0 else: res[i] = weights[i] return res # TEST N = 10 X = rnd.randint( 1,25, (4,N)) print dim_weight(X) I want to implement the dim_weight() function effectively. But I'm not sure how to construct a numpy expression that does the same think for me. I got to this point: def dim_weight(X): weights = X[0] volumes = X[1]*X[2]*X[3] dweights = volumes/194.0 return ( weights[weights>5184] , dweights[weights<=5184] ) # ??? I don't know how to preserve the order of the elements and return the result in one array. Maybe I need to do create a matrix multiply matrix? But don't know how. Thanks, Laszlo -- This message has been scanned for viruses and dangerous content by MailScanner, and is believed to be clean.

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Question about numpy.random, especially .poisson
by Russell E. Owen 19 Apr '11

19 Apr '11

I stumbled across code that looks like this: imageArr = # a 2-d array of floats noiseArr = numpy.random.poisson(imageArr) This works fine in numpy 1.5.1 and seems to do what I would hope: return an array of random ints whose "expectation of interval" is set by the corresponding element of the input array. Very nice! However, I can't find any documentation supporting this usage. The standard help says: poisson(lam=1.0, size=None) Draw samples from a Poisson distribution. The Poisson distribution is the limit of the Binomial distribution for large N. Parameters ---------- lam : float Expectation of interval, should be >= 0. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. Which suggest that Iam must be a scalar. So... is the usage of passing in an array for Iam actually supported/safe to use? And is there some general rule I could have used to predict that? I'm not complaining -- quite the opposite. But I'd hate to code up something that uses an unsafe API, and I'd also like to be able to predict nifty features like this to get the most out of numpy. -- Russell

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[ANN] Courses in Colorado: "Introduction to Python and Python for Scientists and Engineers"
by Mike Müller 19 Apr '11

19 Apr '11

Python Course in Golden, CO, USA ================================ Introduction to Python and Python for Scientists and Engineers -------------------------------------------------------------- June 3 - 4, 2011 Introduction to Python June 5, 2011 Python for Scientists and Engineers Both courses can be booked individually or together. Venue: Colorado School of Mines, Golden, CO (20 minutes west of Denver) Trainer: Mike Müller Target Audience --------------- The introductory course is designed for people with basic programming background. Since it is a general introduction to Python it is suitable for everybody interested in Python. The scientist's course assumes a working knowledge of Python. You will be fine if you take the two-day introduction before hand. The topics are of general interest for scientists and engineers. Even though some examples come from the groundwater modeling domain, they are easy to understand for people without prior knowledge in this field. About the Trainer ----------------- Mike Müller, has been teaching Python since 2004. He is the founder of Python Academy and regularly gives open and in-house Python courses as well as tutorials at PyCon US, OSCON, EuroSciPy and PyCon Asia-Pacific. More Information and Course Registration ---------------------------------------- http://igwmc.mines.edu/short-course/intro_python.html -- Mike mmueller(a)python-academy.de

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Re: [Numpy-discussion] NumPy-Discussion Digest, Vol 55, Issue 42
by Ken Basye 17 Apr '11

17 Apr '11

I think this does what you want: def dim_weight(x): weights = x[0] volumes = x[1]*x[2]*x[3] return np.where(volumes>5184, volumes / 194.0, weights) Best, Ken On 4/17/11 1:00 PM, Laszlo Nagy <gandalf(a)shopzeus.com> wrote: > Message: 1 > Date: Sat, 16 Apr 2011 21:08:55 +0200 > From: Laszlo Nagy<gandalf(a)shopzeus.com> > Subject: [Numpy-discussion] Beginner's question > To: Discussion of Numerical Python<numpy-discussion(a)scipy.org> > Message-ID:<4DA9E947.7060209(a)shopzeus.com> > Content-Type: text/plain; charset=ISO-8859-2; format=flowed > > > Hi All, > > I have this example program: > > import numpy as np > import numpy.random as rnd > > def dim_weight(X): > weights = X[0] > volumes = X[1]*X[2]*X[3] > res = np.empty(len(volumes), dtype=np.double) > for i,v in enumerate(volumes): > if v>5184: > res[i] = v/194.0 > else: > res[i] = weights[i] > return res > > > # TEST > N = 10 > X = rnd.randint( 1,25, (4,N)) > print dim_weight(X) > > I want to implement the dim_weight() function effectively. But I'm not > sure how to construct a numpy expression that does the same think for me. > > I got to this point: > > def dim_weight(X): > weights = X[0] > volumes = X[1]*X[2]*X[3] > dweights = volumes/194.0 > return ( weights[weights>5184] , dweights[weights<=5184] ) # ??? > > I don't know how to preserve the order of the elements and return the > result in one array. Maybe I need to do create a matrix multiply matrix? > But don't know how. > > Thanks, > > Laszlo >

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Unable to clone git repository
by Dave 17 Apr '11

17 Apr '11

Disclaimer: I'm on windows (Win7 x64) Following the instructions at: http://docs.scipy.org/doc/numpy/dev/gitwash/following_latest.html I got the following (rather unhelpful) error message: C:\dev\src>git clone git://github.com/numpy/numpy.git Cloning into numpy... github.com[0: 207.97.227.239]: errno=No error fatal: unable to connect a socket (No error) After a bit of googling I got the vague impression that this may possibly be to do with work blocking access? Fortunately I was able clone the read only https url - https://github.com/numpy/numpy C:\dev\src>git clone https://github.com/numpy/numpy.git Cloning into numpy... remote: Counting objects: 60461, done. remote: Compressing objects: 100% (13476/13476), done. remote: Total 60461 (delta 47928), reused 58235 (delta 46157) Receiving objects: 100% (60461/60461), 19.41 MiB | 2.66 MiB/s, done. Resolving deltas: 100% (47928/47928), done. I would suggest that since they are "the instructions if you just want to follow the latest NumPy source, but you don’t need to do any development" that providing the https url as an alternative would be helpful. Perhaps something like: >From the command line: git clone git://github.com/numpy/numpy.git You should now have a copy of the code tree in the new `numpy` directory. If this doesn't work you can try the alternative read-only url: git clone https://github.com/numpy/numpy.git

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an array become a matrix after an element assignment
by Forrest Sheng Bao 17 Apr '11

17 Apr '11

hi, I encountered a very weird problem tonight. First I created an array and did element-wise multiplication on it: >>> a=zeros((3,3)) + eye(3) >>> a*a array([[ 1., 0., 0.], [ 0., 1., 0.], [ 0., 0., 1.]]) Then i assigned a value to one of the element: >>> a[1,2]=4 Finally, the '*' operator was interpreted as a matrix multiplication >>> a*a array([[ 1., 0., 0.], [ 0., 1., 16.], [ 0., 0., 1.]]) The return types of zeros() and eye() are both arrays. The even weird thing happened later. When I do the same thing in another program (obtaining an adjacency matrix and computing graph powers), my "matrix" was considered as an ''array." So, why could an element assignment turn an array into a matrix? And sometimes not? Cheers, Forrest

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Cython error with complex numpy array
by arrigo 16 Apr '11

16 Apr '11

I am trying to use Cython to speed up some calculation with complex numpy arrays and I am getting this error: C:>python setup_post.py build_ext --inplace running build_ext cythoning SVDc_post.pyx to SVDc_post.c Error converting Pyrex file to C: ------------------------------------------------------------ ... # static inline Real csq(cComplex x) { return creal(x*conj(x)); } cdef inline double csq(x): return real(x*x.conjugate()) def SVDc2(int m, int n, ^ ------------------------------------------------------------ C:\SVDc_post.pyx:11:0: Compiler crash in IntroduceBufferAuxiliaryVars ModuleNode.body = StatListNode(SVDc_post.pyx:2:0) StatListNode.stats[4] = StatListNode(SVDc_post.pyx:11:0) StatListNode.stats[0] = DefNode(SVDc_post.pyx:11:0, modifiers = [...]/0, name = u'SVDc2', num_required_args = 7, reqd_kw_flags_cname = '0') The code is below (I removed all the code not relevant to this error): import numpy as np cimport numpy as np ctypedef np.float64_t dtype_t cdef inline double csq(x): return real(x*x.conjugate()) def SVDc2(int m, int n, np.ndarray[np.complex128_t, ndim = 2] Ao, np.ndarray[np.float64_t, ndim = 1] d, np.ndarray[np.complex128_t, ndim = 2] Vo, np.ndarray[np.complex128_t, ndim = 2] Wo, int sort): cdef dtype_t EPS = 1.5e-31 cdef dtype_t DBL_EPS = 2.2204460492503131e-16 cdef int maxsweeps = 50 cdef int err = True cdef int rev = ((m - n) >> 15) & 1 cdef int nm = min(m, n) cdef int nx = max(m, n) cdef dtype_t red = .01 / (nx * nx * nx * nx) cdef int ldV = nx cdef int ldW = nx cdef int ldA = nx cdef dtype_t thresh = 0.0 cdef np.ndarray[np.complex128_t, ndim=3] VW = zeros((3, nx, nx), dtype=complex128_t) cdef np.ndarray[np.int32, ndim=1] ppi = empty(nm, dtype=int32) for p in range(0, nx): VW[0, p, p] = 1 for p in range(0, nx): VW[1, p, p] = 1 if rev: for q in range(0, m): for p in range(0, n): VW[2, p, q] = Ao[q, p] else: for q in range(0,m): for p in range(0,n): VW[2, q, p] = Ao[q, p] return I was wondering if I need a special setup.py when using numpy arrays. Any ideas? Thanks, -Arrigo

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dtype itemsize stored in 32 bit value?
by russel 15 Apr '11

15 Apr '11

Is this a bug? In [81]: a = np.dtype([('field', 'u1', (2**31,))]) In [82]: a.itemsize Out[82]: -2147483648

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