Mailman 3 March 2019 - SciPy-User

How to display a RGB image (MFISH)?
by jp April 3, 2023

April 3, 2023

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 July 28, 2022

July 28, 2022

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Help required regding Eigenvectors
by Sudheer Phani March 14, 2022

March 14, 2022

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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ANN: SfePy 2019.1
by Robert Cimrman March 26, 2019

March 26, 2019

I am pleased to announce release 2019.1 of SfePy. Description ----------- SfePy (simple finite elements in Python) is a software for solving systems of coupled partial differential equations by the finite element method or by the isogeometric analysis (limited support). It is distributed under the new BSD license. Home page: http://sfepy.org Mailing list: https://mail.python.org/mm3/mailman3/lists/sfepy.python.org/ Git (source) repository, issue tracker: https://github.com/sfepy/sfepy Highlights of this release -------------------------- - automatic fallback for linear solvers - quadratic eigenvalue problem solver For full release notes see [1]. Cheers, Robert Cimrman [1] http://docs.sfepy.org/doc/release_notes.html#id1 --- Contributors to this release in alphabetical order: Robert Cimrman Lubos Kejzlar Vladimir Lukes

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Re: [SciPy-User] Discrete Fourier transforms (scipy.fftpack)- radially averaged power spectrum(RAPS)
by Eyal Saiet March 22, 2019

March 22, 2019

Checking if this email made it through the scipy community!? On Sat, Mar 16, 2019 at 4:49 PM Eyal Saiet <ejsaiet(a)alaska.edu> wrote: > Hello, > The scipy.fftpack is quite a comprehensive toolset. Nonetheless, I am > scratching my head why I cannot find a radially averaged power > spectrum(RAPS) tool in scipy.fftpack which seems a natural complementary > tool for an FFT toolset. Do people just write the function for themselves? > Or is it somewhere else already? > > * The radially averaged power spectrum(RAPS) is s the > direction-independent mean spectrum, i.e. the average of all possible > directional power spectra. The radially averaged power spectrum provides a > convenient means to view and compare information contained in 2-D spectra > in 1-D. This function computes and plots the RAPS of an input matrix (does > not consider corner values outside averaging radius). The image can be > rectangular but must be 2-D (e.g., multi-color channel data is not > supported). The spatial resolution of the data is also specified (https://www.mathworks.com/matlabcentral/fileexchange/23636-radially-average… > ) > > *This tool was in Astropy 2.7 but I cannot find it Astropy 3.1.... > > Thanks > -- >

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Fwd: Discrete Fourier transforms (scipy.fftpack)- radially averaged power spectrum(RAPS)
by Eyal Saiet March 17, 2019

March 17, 2019

Hello, The scipy.fftpack is quite a comprehensive toolset. Nonetheless, I am scratching my head why I cannot find a radially averaged power spectrum(RAPS) tool in scipy.fftpack which seems a natural complementary tool for an FFT toolset. Do people just write the function for themselves? Or is it somewhere else already? * The radially averaged power spectrum(RAPS) is s the direction-independent mean spectrum, i.e. the average of all possible directional power spectra. The radially averaged power spectrum provides a convenient means to view and compare information contained in 2-D spectra in 1-D. This function computes and plots the RAPS of an input matrix (does not consider corner values outside averaging radius). The image can be rectangular but must be 2-D (e.g., multi-color channel data is not supported). The spatial resolution of the data is also specified (https://www.mathworks.com/matlabcentral/fileexchange/23636-radially-average… ) *This tool was in Astropy 2.7 but I cannot find it Astropy 3.1.... Thanks -- Eyal

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Root Locus Analysis
by Fábio Molinar March 9, 2019

March 9, 2019

Hi everyone, I am a Controls Engineer who used to use Matlab quite frequently at the University to analyze, among other things, system's stability. I am now reading again some theoretical books on control theory to refresh my mind again with some of the theories and concepts I haven't used for a while. But since I became a python programmer and enthusiast, I am doing this review while using SciPy to program some tests and examples. And I am really enjoying using SciPy to code some of my scripts. Although I just started using it, I can't stress enough how much fun and how useful the scipy.signal package is. But, during my theory review I came upon the Root Locus Analysis and, to my disappointment, I didn't find support for it on the scipy.signal package. *Is anyone aware if there is someone working on implementing Root Locus analysis into the scipy.signal package*? I know that there is another python package called "control" which has support for many control analysis tools, including Root Locus analysis, but I would like to avoid installing yet another package into my python environment. Especially because installing the "control" python package isn't that straight-forward and requires a bunch of other packages/libraries. Yours sincerely, *Fábio Thomaz Molinar*

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On numpy.pad(array, pad_width, mode, **kwargs)
by Virgil Stokes March 5, 2019

March 5, 2019

One mode of padding is 'mean'. However, it does not return the actual mean of the array --- it returns the rounded value of the mean! example: a = np.array([1, 2, -1, 4, -3]) np.mean(a) = 0.6 np.pad(a, (2,), 'mean') = [ 1 1 1 2 -1 4 -3 1 1] The documentation that I found at: https://docs.scipy.org/doc/numpy/reference/generated/numpy.pad.html shows: ‘mean’ - Pads with the mean value of all or part of the vector along each axis. It actually Pads with the rounded mean value. IMHO it should return the "float_" value. If the SciPy team does not agree, then the documentation should be changed to show what it actually returns. --V IMHO it should När du har kontakt med oss på Uppsala universitet med e-post så innebär det att vi behandlar dina personuppgifter. För att läsa mer om hur vi gör det kan du läsa här: http://www.uu.se/om-uu/dataskydd-personuppgifter/ E-mailing Uppsala University means that we will process your personal data. For more information on how this is performed, please read here: http://www.uu.se/en/about-uu/data-protection-policy

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