Mailman 3 August 2016 - NumPy-Discussion

test_closing_fid (in test_io.py) on PyPy
by Matti Picus 05 Aug '16

05 Aug '16

test_closing_fid essentially calls this to ensure close() is called when a NpzFile object goes out of context: for i in range(1, 1025): np.load(tmp)["data"] This raises a ResourceWarning on python 3, and fails on pypy since the garbage collector works differently. It seems to be a classic example of wrongly using np.load(), but I'm sure it is a common use case. I can submit a pull request to skip on pypy, or should this be solved in a more substantial way? Matti

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Re: [Numpy-discussion] scipy curve_fit variable list of optimisation parameters
by Siegfried Gonzi 03 Aug '16

03 Aug '16

On 3 Aug 2016, at 13:00, numpy-discussion-request(a)scipy.org wrote: > Message: 3 > Date: Tue, 2 Aug 2016 22:50:42 +0100 > From: Evgeni Burovski <evgeny.burovskiy(a)gmail.com> > To: Discussion of Numerical Python <numpy-discussion(a)scipy.org> > Subject: Re: [Numpy-discussion] scipy curve_fit variable list of > optimisation parameters > Message-ID: > <CAMRo0ivCS8HqR6Pyo9-wvnhboPGYzaFwfZNmVwE5Q5NTXna9Dg(a)mail.gmail.com> > Content-Type: text/plain; charset=UTF-8 > > > You can use `leastsq` or `least_squares` directly: they both accept an > array of parameters. > > BTW, since all of these functions are actually in scipy, you might > want to redirect this discussion to the scipy-user mailing list. Hi all I found the solution in the following thread: http://stackoverflow.com/questions/28969611/multiple-arguments-in-python One has to call curve_fit with 'p0' (giving curve_fit a clue about the unknown number of variables) I changed func2 to (note the *): def func2( x, *a ): # Bessel function tmp = scipy.special.j0( x[:,:] ) return np.dot( tmp[:,:] , a[:] ) and call it: N = number of optimisation parameters popt = scipy.optimize.curve_fit( func2, x, yi , p0=[1.0]*N) Regards, Siegfried Gonzi Met Office, Exeter, UK -- The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336.

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scipy curve_fit variable list of optimisation parameters
by Siegfried Gonzi 02 Aug '16

02 Aug '16

Hi all Does anyone know how to invoke curve_fit with a variable number of parameters, e.g. a1 to a10 without writing it out, e.g. def func2( x, a1,a2,a3,a4 ): # Bessel function tmp = scipy.special.j0( x[:,:] ) return np.dot( tmp[:,:] , np.array( [a1,a2,a3,a4] ) ### yi = M measurements (.e.g M=20) ### x = M (=20) rows of N (=4) columns popt = scipy.optimize.curve_fit( func2, x, yi ) I'd like to get *1 single vector* (in this case of size 4) of optimised A(i) values. The function I am trying to minimise (.e.g F(r) is a vector of 20 model measurements): F(r) = SUM_i_to_N [ A(i) * bessel_function_J0(i * r) ] Thanks, Siegfried Gonzi -- The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336.

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