Mailman 3 October 2016 - 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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Big performance hit when using frozen distributions on scipy 0.16.0
by Nicolas Chopin Oct. 30, 2016

Oct. 30, 2016

Hi list, I'm working on a package that does some complicate Monte Carlo experiments. The package passes around frozen distributions quite a lot. Trying to understand why certain parts were so slow, I did a bit of profiling, and stumbled upon this: > %timeit x = scipy.stats.norm.rvs(size=1000) > 10000 loops, best of 3: 49.3 µs per loop > %timeit dist = scipy.stats.norm(); x = dist.rvs(size=1000) > 1000 loops, best of 3: 512 µs per loop So a x10 penalty when using a frozen dist, even if the size of the simulated vector is 1000. This is using scipy 0.16.0 on Ubuntu 16.04. I cannot replicate this problem on another machine with scipy 0.13.3 and Ubuntu 14.04 (there is a penalty, but it's much smaller). In the profiler, I can see that a lot of time is spent doing string operations (such as expand_tabs) in order to generate the doc. In the source, I see that this may depend on a certain -00 flag??? I do realise that instantiating a frozen distribution requires some argument checking and what not, but here it looks too expensive. For my package, this amounts to hours spent on ... tab extensions? Anyway, I'd like to ask (a) is this a known problem? I could not find anything on-line about this. (b) Is this going to be fixed in some future version of scipy? (c) is there a way to fix this with *this* version of scipy using this flag mentioned in the source, and then how? (c) or should I instead re-define manually my own distributions objects? (it's really convenient for what I'm trying to do to define distributions as objects with methods rvs, logpdf, and so on). Many thanks for reading this! :-) All the best

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Generating PDF from 'sampled' pdf
by Jared Vacanti Oct. 25, 2016

Oct. 25, 2016

I have an approximation of a PDF (by taking the derivative of an approximation of the CDF) but can't get scipy to 'interpolate' a distribution from this data. I conceptually understand the difficulty because I'm not looking at observations, but already an attempt at the PDF. I wrote an SO question here asking the same thing - http://scicomp.stackexchange.com/questions/25311/python-differentiating-cub… . The link contains a SSCCE with actual data, but I would like to be able to apply this to other areas of research as well. Can I fit a probability density function to my attempted "sampled" collection of one? Jared Vacanti

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is doc for stats.chi2 correct?
by Neal Becker Oct. 25, 2016

Oct. 25, 2016

The expression for pdf given here https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.chi2.html#… doesn't seem to match that given in other references: https://en.wikipedia.org/wiki/Chi-squared_distribution or Proakis: Digital Communications, 3rd edition, pg 42

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how to treat an invalid value, in signal/filter_design.py
by R Schumacher Oct. 21, 2016

Oct. 21, 2016

In an attempt to computationally invert the effect of an analog RC filter on a data set and reconstruct the signal prior to the analog front end, a co-worker suggested: "Mathematically, you just reverse the a and b parameters. Then the zeros become the poles, but if the new poles are not inside the unit circle, the filter is not stable." So then to "stabilize" the poles' issue seen, I test for the DIV/0 error and set it to 2./N+0.j in scipy/signal/filter_design.py ~ line 244 d = polyval(a[::-1], zm1) if d[0]==0.0+0.j: d[0] = 2./N+0.j h = polyval(b[::-1], zm1) / d - Question is, is this a mathematically valid treatment? - Is there a better way to invert a Butterworth filter, or work with the DIV/0 that occurs without modifying the signal library? I noted d[0] > 2./N+0.j makes the zero bin result spike low; 2/N gives a reasonable "extension" of the response curve. The process in general causes a near-zero offset however, which I remove with a high pass now; In an full FFT of a ~megasample one can see that the first 5 bins have run away. An example attached... Ray Schumacher Programmer/Consultant Ray Schumacher Programmer/Consultant PO Box 182, Pine Valley, CA 91962 (858)248-7232 http://rjs.org/

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Abandoned SciPy Google Group
by Denis Akhiyarov Oct. 19, 2016

Oct. 19, 2016

Why is this google group for scipy not synced with this mailing list? There is a long list of unresolved questions. https://groups.google.com/forum/m/#!forum/scipy-user

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return values of scipy.signal.deconvolve
by Jose Guzman Oct. 18, 2016

Oct. 18, 2016

Dear Scipy users, I’m trying to understand what scipy.signal.deconvolve is doing… so I may not have the concepts very clear here… basically I’m trying to compute the times where an event is occurring based on a template. I created a notebook to illustrate it: https://github.com/JoseGuzman/myIPythonNotebooks/blob/master/NEURON/Deconvo… I have a trace where the signal is repeated several times. When I deconvolve the trace with the signal, I cannot see relationship between the number of events and the quotient that scipy.signal.deconvolve is returning. I would appreciate some hints here, Thank you Jose

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[ANN] SciPy India 2016: call for papers
by Prabhu Ramachandran Oct. 17, 2016

Oct. 17, 2016

Hello, We are pleased to announce the SciPy India conference 2016. SciPy India is an annual conference on using Python for research and education. The conference is currently in its eighth year and will be held at IIT Bombay on 10th and 11th December, 2016. The registration and call for papers are open. Please visit http://scipy.in to register and submit your proposals. Please spread the word! Call for Papers ============= We look forward to your submissions on the use of Python for scientific computing and education. This includes pedagogy, exploration, modeling, and analysis from both applied and developmental perspectives. We welcome contributions from academia as well as industry. For details on the paper submission please see here: http://scipy.in/2016/cfp/ Important Dates ================ - Call for proposals end: 20th November 2016 - List of accepted proposals will be published: 1st December 2016. We look forward to seeing you at SciPy India. Regards, Prabhu Ramachandran (For the SciPy organizing team)

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