Mailman 3 December 2016 - SciPy-User

How to display a RGB image (MFISH)?
by jp 02 Apr '23

02 Apr '23

Hi, I have written a script to try to combine five images into a three channels RGB images<http://dip4fish.blogspot.com/2011/05/trying-to-combine-mfish-images-into-rg…> . The resulting RGB image looks like a grey scale image instead of a color one. I have three np.array Rnorm, Gnorm, Bnorm which are copied into a rgb array: *rgb = np.zeros((shape[0],shape[1],3),dtype=float) mxr=np.max(R) mxg=np.max(G) mxb=np.max(B) Rnorm=np.uint8((255*(R/mxr)))Gnorm=np.uint8((255*(R/mxg)))Bnorm=np.uint8((255*(R/mxb)))#copy each RGB component in an RGB array rgb[:,:,0]=Rnorm rgb[:,:,1]=Gnorm rgb[:,:,2]=Bnorm* *pylab.subplot(224, aspect='equal',frameon=False, xticks=[], yticks=[]) pylab.imshow(rgb) pylab.show()* Any advice? Thank you Jean-Patrick

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Invitation to connect on LinkedIn
by Jelle Feringa via LinkedIn 28 Jul '22

28 Jul '22

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Help required regding Eigenvectors
by Sudheer Phani 14 Mar '22

14 Mar '22

Hello All, I have been encountering some strange problems with the calculation of eigen vectors using python. The problem is with the sign of the eigen vectors calculated. On Very few occasions the eigen vectors calculated using python have an opposite sign than the actual eigenvectors, though the magnitude is correct. Can any one of you plz help me in this regard and suggest me some solution if you have encoutered similar problems . Any help in this regd is appreciated Thanks in Advace regds Sudheer

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scipy.optimize.root with method = 'lm' problem
by William Heymann 28 Dec '16

28 Dec '16

I am using scipy.optimize.root with method = 'lm' and jac=True and the descent algorithm looks like it is taking steps that are far too large. I have 10 variables and my goal function returns a vector of 4374 outputs and 4374x10 for the jacobian. The initial jacobian matches the same one I generated when I use lsqnonlin in matlab and that works just fine. Instead with scipy I am getting no changes at all for the first two iterations and then suddenly a huge jump to basically the upper end of the double range which seems pretty extreme. I would really like to make this work with scipy and I have my function along with the exact derivatives for the jacobian computed with AD. I have also looked at the singular values of the jacobian and they are all positive and non-zero so the system should be locally convex at least. This is the call I make to scipy and it seems reasonable sol = scipy.optimize.root(residual, start, args=(large, small, weight, data), method='lm', jac=True, options={'ftol':1e-6, 'xtol':1e-6}) Thank you

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NumPy 1.12.0rc1
by Charles R Harris 19 Dec '16

19 Dec '16

Hi All, I am pleased to announce the release of NumPy 1.12.0rc1. This release supports Python 2.7 and 3.4 - 3.6 and is the result of 406 pull requests submitted by 139 contributors and comprises a large number of fixes and improvements. Among the many improvements it is difficult to pick out just a few as standing above the others, but the following may be of particular interest or indicate areas likely to have future consequences. * Order of operations in ``np.einsum`` can now be optimized for large speed improvements. * New ``signature`` argument to ``np.vectorize`` for vectorizing with core dimensions. * The ``keepdims`` argument was added to many functions. * New context manager for testing warnings * Support for BLIS in numpy.distutils * Much improved support for PyPy (not yet finished) The release notes are quite sizable and rather than put them inline I've attached them as a file. They may also be viewed at Github <https://github.com/numpy/numpy/releases/tag/v1.12.0rc1>. Zip files and tarballs may also be found the Github link. Wheels and a zip archive are available from PyPI, which is the recommended method of installation. Cheers, Charles Harris

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Scipy.signal.spectrogram() thread safety
by Rüdiger Berlich 11 Dec '16

11 Dec '16

Hi there, I would like to do a STFT for the independent data from multiple files in parallel in multiple threads, using the scipy.signal.spectrogram() function. No data in the arguments to this function is shared between the threads, so I would expect this usage pattern to be thread-safe („reentrant“). First tests have not revealed any problems with 4 parallel calls to spectrogram(). I could not find information on this topic in the documentation of the spectrogram function. Any help would be appreaciated. Kind Regards, Beet

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回复： About three dimensional interpolation
by 豆腐 06 Dec '16

06 Dec '16

Thank you very much! I found RegularGridInterpolator was what I wanted. ------------------ 原始邮件 ------------------ 发件人: "Paul Hobson";<pmhobson(a)gmail.com>; 发送时间: 2016年12月7日(星期三) 中午11:15 收件人: "SciPy Users List"<scipy-user(a)python.org>; 抄送: "scipy-dev"<scipy-dev(a)scipy.org>; 主题: Re: [SciPy-User] About three dimensional interpolation I can't comment on feature parity with matlab, but here's the documentation for the N-dimensional interpolators:https://docs.scipy.org/doc/scipy-0.18.1/reference/interpolate… On Tue, Dec 6, 2016 at 5:07 PM, 豆腐 <39256912(a)qq.com> wrote: Hello, I want to ask sicpy have a three-dimensional interpolation module? Just like MATLAB VI=interp3(X,Y,Z,V,XI,YI,ZI). THANKS！ befor after _______________________________________________ SciPy-User mailing list SciPy-User(a)python.org https://mail.python.org/mailman/listinfo/scipy-user

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About three dimensional interpolation
by 豆腐 06 Dec '16

06 Dec '16

Hello, I want to ask sicpy have a three-dimensional interpolation module? Just like MATLAB VI=interp3(X,Y,Z,V,XI,YI,ZI). THANKS！ befor after

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