Mailman 3 July 2011 - SciPy-User

maximally sparse subset of points
by Gustavo Goretkin 29 Jul '11

29 Jul '11

I have a dataset of N points (in 4 dimensions) and I'd like to select a smaller subset, size M, of those points that are maximally spread out. An approximation is fine. Other than the K-d tree, is there anything in SciPy or other Python module to help accomplish this? Thanks

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binom pdf error
by Antonio Ingargiola 28 Jul '11

28 Jul '11

Hi to the list, I got an error using the pdf method on the binom distribution. Same error happens on scipy 0.8 and scipy 0.9 (respectively ubuntu distribution and pythonxy on windows). The error is the following: In [1]: from scipy.stats.distributions import binom In [2]: b = binom(20,0.8) In [3]: b.rvs() Out[3]: 17 In [4]: b.pdf(2) --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) C:\Users\anto\.xy\startups\<ipython console> in <module>() C:\Python26\lib\site-packages\scipy\stats\distributions.pyc in pdf(self, x) 333 334 def pdf(self, x): #raises AttributeError in frozen discrete distribution --> 335 return self.dist.pdf(x, *self.args, **self.kwds) 336 337 def cdf(self, x): AttributeError: 'binom_gen' object has no attribute 'pdf' In [5]: Is this known problem? How can I get the binomial pdf, is there a workaround? Many thanks, Antonio

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Some problem with defining parameters in f2py
by timofey chernishev 28 Jul '11

28 Jul '11

Hi. I use gfortran 4.4.3, f2py Version: 2, numpy Version: 1.3.0. when i try: subroutine poiss1d(phi0,pts,n_q,phi1) real,parameter::pi=3.14159265358979323846264338327950288,h=0.01,e=4.8E-10 real,parameter::Cq=2.0*pi*h**2 *e,Ca=0.5,Cd=0.5 ... end subroutine poiss1d and have an error: "Error: Parameter 'pi' at (1) has not been declared or is a variable, which does not reduce to a constant expression" but, when e is moved to the next string: subroutine poiss1d(phi0,pts,n_q,phi1) real,parameter::pi=3.14159265358979323846264338327950288,h=0.01 real,parameter::e=4.8E-10 real,parameter::Cq=2.0*pi*h**2 *e,Ca=0.5,Cd=0.5 ... end subroutine poiss1d all works -- why so?

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numpy mean
by questions anon 28 Jul '11

28 Jul '11

Hi All, I thought this would be a relatively easy thing to do but the more I look the more confused I become! I have a netcdf file containing hourly temperature data for given region for a month. I would like to find the mean/average of particular periods (i.e. 3 hours). e.g. this is what I would like: array1=[2,4,8] array2=[4,8,12] array3=[9,3,15] meanofarrays=np.mean(array1,array2,array3) print meanofarrays >>>[5,5,11] Is there a routine that will do what I am after? If not I seem to be able to sum the arrays together and then divide by another array, but I will need to produce an array to match the extent and all values will need to be equal to the number of arrays I have summed. Can anyone help with producing this array? i.e. sumofarray=[15,15,35] numberofarrays=[3,3,3] meanofarrays=np.divide[sumofarrays,numberofarrays] print meanofarrays >>>[5,5,11] Any feedback will be greatly appreciated!!!

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New article about group by functionality
by Wes McKinney 27 Jul '11

27 Jul '11

This may be of interest to many: http://wesmckinney.com/blog/?p=125 Would be interest to get thoughts on the matter as it relates to NumPy and other related functionality that's needed. - Wes

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unravel_index for pdist?
by Benjamin Root 27 Jul '11

27 Jul '11

Hello, I typically use pdist() from scipy.spatial.distance to keep some of my scripts lean wrt memory usage. However, determining which pairs of points a distance value refers to is a bit tricky. I was wondering if a "punravel_index()" function might be welcomed? Here is my algorithm I came up with to perform this action for a single result. I haven't tested it for anything more general than that. *pntCnt* is the number of points that pdist() was used for. *index* is an index from a function like "np.argmax(pdist(p))". rowcnts = np.cumsum(xrange(pntCnt - 1, 0, -1)) row = np.searchsorted(rowcnts, index) col = pntCnt - (rowcnts[row] - index) At the very least, I hope that this is helpful to anybody else using pdist. Ben Root

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Help!!!!!! having problems with ODEINT
by Laura Matrajt 27 Jul '11

27 Jul '11

Hi all, I am working with a system of 16 differential equations that simulates an epidemic in a city. Because there are many cities interacting with each other, I need to run my ode's for a single day, stop them, modify the initial conditions and run them again. Because the ode is running only for a day, I defined my tspan to have only two points, like this: tspan = tspan = linspace(day, day+1, 2) I wrote my equations in Python and I am using scipy.odeint to solve it. Here is my code: def advanceODEoneDay(self,day): #rename variables for convenience N0, N1 = self.children, self.adults S0,S1,A0,A1,I0,I1,RA0,RA1,RI0,RI1 = self.S0,self.S1,self.A0,self.A1,self.I0,self.I1,self.RA0,self.RA1,self.RI0,self.RI1 #create a vector of times for integration: tspan = linspace(day, day+1, 2) #set initial conditions. To do this, I need to look in the array in the day initCond = [S0[day,0], S0[day,1], S1[day,0], S1[day,1], A0[day,0], A0[day,1], A1[day,0], A1[day,1], I0[day,0], I0[day,1], I1[day,0], I1[day,1], RA0[day,0], RA1[day,0], RI0[day,0], RI1[day,0] ] #run the ode: sir_sol = odeint(sir2groups,initCond, tspan, args=(self.p,self.C,self.m,self.VEs,self.VEi,self.VEp,N0,N1))#,self.gamma,self.rho)) Most of the time, it works just fine. However, there are some times where the following message appears: Excess work done on this call (perhaps wrong Dfun type). Run with full_output = 1 to get quantitative information. lsoda-- at current t (=r1), mxstep (=i1) steps taken on this call before reaching tout In above message, I1 = 500 In above message, R1 = 0.1170754095027E+03 I should mention that it is NOT only a warning. This is repeated over and over (thousands of times) and then it will break the rest of my code Ok, after searching in this mailing list, someone else posted a similar warning message and it was suggested to him that " *In your case, you might simply be computing for to coarse a mesh in t,** so "too much work" has to be done for each step. " *Is this what is happening to me? the problem is that I don't get this error every single time, so I don't even know how to run it with full_output = 1 to get the info... I really don't know what to do. Any help will be very very very very appreciated! thank you very * * -- Laura

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Problem with scipy.signal
by Hugo Silva 27 Jul '11

27 Jul '11

Hi, I've just installed Python 2.7 on a MacBook Pro 2.7GHz i7 / 8Gb / Mac OS 10.6.8 and I'm facing the following error when trying to import scipy.signal: ImportError: dlopen(/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/scipy/signal/sigtools.so, 2): no suitable image found. Did find: /Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/scipy/signal/sigtools.so: no matching architecture in universal wrapper Has anyone faced the same problem or can share some insight on what can be a possible solution for this issue? Thank's in advance, Hugo Silva

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Questions about scipy.optimize.anneal
by Jason Heeris 27 Jul '11

27 Jul '11

Hi, I have a few questions about scipy.optimize.anneal. I'm using 0.9.0 under Debian Squeeze with Python 2.6.6, but the documentation for anneal is the same as for 0.10.0. Firstly, I'm confused about the return values — the docs say that the return values are: xmin (ndarray), retval (int), Jmin (float), T (float), feval (int), iters (int), accept (int) ...however, running the attached script gives (array([-10.16682436, 6.46000538]), 12.045579564382059, 0.72724961763274643, 851, 16, 180, 5) [<type 'numpy.ndarray'>, <type 'numpy.float64'>, <type 'numpy.float64'>, <type 'int'>, <type 'int'>, <type 'int'>, <type 'int'>] Note that the types don't match and there's no value that could correspond to the documented options for retval! Am I reading things wrong, or are these inconsistent with the docs? Secondly, since annealing is a random process, is there any way to control the source of random numbers it uses? Should I seed some global RNG, and if so, which one? Thirdly, note that the script actually fails to find the optimal parameters, despite being given them as the starting point. I know that annealing is a far from reliable process, but this behaviour still seems a little strange given the simplicity of the function I'm using here. Are there additional constraints I should be supplying, or some other way to guide the annealing process? Fourthly, in my actual application I have a function to optimise that is invariant to the overall scale of the inputs, ie. f(a*array([x,y,z])) is the same for all non-zero values of "a". Is there a risk of anneal failing because of this degeneracy? Is there a way to structure the problem to avoid this? (Note that any component could legitimately be zero, so I can't just fix x = 1 ... I think). Cheers, Jason

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VODE with BDF method
by lechtlr 26 Jul '11

26 Jul '11

Is there any option in Scipy to use "internally generated full Jacobian" with VODE/BDF method for stiff systems ? Any help would greatly be appreciated. -Lex

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