Mailman 3 August 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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Is the second half of np.fft.fft the negative frequencies?
by Matti Viljamaa 31 Aug '16

31 Aug '16

I’m having the following problem http://dsp.stackexchange.com/questions/33982/problem-with-windowing-method-… <http://dsp.stackexchange.com/questions/33982/problem-with-windowing-method-…> My question is: in http://i.stack.imgur.com/fYFvx.png <http://i.stack.imgur.com/fYFvx.png> are samples [0, 512] the positive frequencies and [513, 1024] the negative frequencies (in two-sided FFT). I.e. that to be correct the plot should actually look like [513, 1024] is [-512, 0] and [0,512] is [0,512], but is this the np.fft.fft way to return the negative frequencies (i.e in the latter half of the fft result)?

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ANN: pybroom 0.2 released
by Antonino Ingargiola 30 Aug '16

30 Aug '16

Hi, version 0.2 of pybroom has been released. See below for the details. What is pybroom? ============== Pybroom is a small python 3+ library for converting collections of fit results (curve fitting or other optimizations) to Pandas DataFrame in tidy format (or long-form) (Wickham 2014). Once fit results are in tidy DataFrames, it is possible to leverage common patterns for tidy data analysis. Furthermore powerful visual explorations using multi-facet plots becomes easy thanks to libraries like seaborn natively supporting tidy DataFrames. Homepage: http://pybroom.readthedocs.io/ Release Notes ============ - Improved support for scipy.optimize fit result. - In addition to list of fit results, pybroom now supports: - dict of fit results, - dict of lists of fit results - any other nested combination of dict and list. - When input contains a dict, pybroom adds “key” column of type pandas.Categorical. - When input contains a list, pybroom adds a “key” column (i.e. list index) of type int64. - Updated and expanded documentation and notebooks. Installation ======== You can install pybroom from PyPI using the following command: pip install pybroom or from conda-forge using: conda install -c conda-forge pybroom Dependencies are python 3.4+, pandas and lmfit (0.9.5+, which in turn requires scipy). However, matplotlib and seaborn are strongly recommended (and necessary to run the example notebooks).

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"obvious" simple linear program with integral constrains solver
by Sebastian Berg 30 Aug '16

30 Aug '16

Hi all, I wanted to solve a small integer problem today. Now I have done this before and I know that there are some options available in python, but I was wondering if there is a solver with support for linear problems with integer constraints available in the more basic "scientific python stack"? Nothing fancy, but something that can be installed quickly (best included in things like Anaconda). Basically is there an "obvious" solution, at least for non-demanding tasks one could/should recommend? Best, Sebastian

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Does/how does ifft expect frequencies (on x-axis)?
by Matti Viljamaa 30 Aug '16

30 Aug '16

I was trying to ifft a filter magnitude response that’s simply drawn into an array of filter length, e.g. 64 samples. So I have the array of 64 samples and this obviously means that the x-axis is in samples, not frequencies. ifft:ing this array does give me something that looks like the impulse response, but I didn’t give ifft any frequency data, just samples 0-64 in the x-axis. What I think I could’ve done is rather specify the magnitude response in 2D array or something so that the frequency runs from [0.0, 1.0] (0.5 corresponding to the Nyquist frequency) and then specify the magnitudes in 64 samples evenly spread on [0.0, 1.0], which is what I _intend_ with my array. So does ifft expect frequencies somehow or does it also work in the way that I used it, i.e. that the frequency response going into it is in samples rather than in frequencies? The functions I’m interested are any ifft in SciPy.

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freqz -style plot of "window design" FIR?
by Matti Viljamaa 29 Aug '16

29 Aug '16

I’m trying to design an arbitrary frequency response filter as described here: http://www.dspguide.com/ch17/1.htm <http://www.dspguide.com/ch17/1.htm> The technique is said to result in an impulse response in time domain and later in to a filter kernel. I’ve been using scipy.signal.freqz to make magnitude response plots: e.g. fs = 44100 # Design a low-pass filter using remez. cutoff = 2000.0 transition_width = 200 bands = np.array([0, cutoff - 0.5*transition_width, cutoff + 0.5*transition_width, fs/2.0]) / fs desired = [1, 0] lpf = remez(513, bands, desired) # Plot the frequency response of the filter. w, h = freqz(lpf) plt.figure(1) plt.plot(fs*w/(2*np.pi), 20*np.log10(abs(h))) plt.xlim(0, fs/2) plt.xlabel('Frequency (Hz)') plt.ylabel('Gain (dB)') plt.grid(True) But my question is, if using the above arbitrary frequency response design technique, would I be able to use freqz? freqz takes as a parameter “numerator of a linear filter” and remez is returning an array of coefficients, which I read to be the same thing. But in the case of the arbitrary frequency response filter, what can I put into freqz? Is filter kernel perhaps the same as coefficients?

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Interpolated Univariate Spline - what are coefs, and how is integral calculated?
by Andrew Nelson 12 Aug '16

12 Aug '16

I have a few questions about interpolate.InterpolatedUnivariateSpline (IUS). My ultimate aim, given an IUS object, a lower definite integration limit, and an area, is to obtain the upper definite integration limit by the fastest route possible. The application is to do with probability distribution functions. The IUS represents the PDF. I can quickly work out the CDF using IUS.integral(a, x) = q, where a is the lower limit of support and x is the value for which you want to work out the CDF. However, I would like the quickest way to calculate the percent probability function (PPF). I.e. given q, work out x. One way used in scipy.stats is to use optimize.brentq to find this value - It's known that the CDF is a monotonically increasing function. It strikes me that brentq might not be the fastest way of achieving this. For example, I could cache the integral at each of the datapoints for the IUS, which would immediately allow me to bracket the location of x. Given that I know the degree of the smoothing spline I am hoping to calculate x quickly if I know the polynomial coefficients of the spline in the bracketing interval. This would done by integrating the polynomial and using the roots of the integral. My question is: Is there a way of getting those polynomial coefficients for each interval from the output of the IUS.get_coeffs method? -- _____________________________________ Dr. Andrew Nelson _____________________________________

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ANN: PhD student position on energy system modelling with EURAC & UNITN (Italy)
by Pietro 10 Aug '16

10 Aug '16

EURAC <http://www.eurac.edu/en/research/technologies/renewableenergy/Pages/default…> in collaboration with the University of Trento <http://www.dicam.unitn.it/en>, propose a PhD on energy system modelling. This opportunity is offered to high-achieving students looking to begin a PhD programme in November 2016. Students from anywhere in the world can apply. Here you can find the PhD studentship description <http://www.unitn.it/alfresco/download/workspace/SpacesStore/35032294-3f05-4…> and the details on how to apply <http://www.unitn.it/en/ateneo/1954/announcement-of-selection>. *TOPIC*: "Multi-dimensional energy system modeling: Methods and tools for the optimization of energy systems considering spatial, temporal and social-economic features" *DEADLINE FOR APPLICATIONS*: 25th August 2016, hrs. 4.00 PM (Italian time) In the application form you must select the B2 - topic. *MANDATORY ATTACHMENTS*: 1. Identifying document (ID): 2. Academic certificate 3. Summary of the degree thesis 4. Curriculum vitae et studiorum 5. Description of the research proposal and statement of purpose in English (maximum 3 pages) *OPTIONAL ATTACHMENTS*: - List of research outputs; - any certificates attesting the knowledge of foreign languages; - any other documents useful to assess the research skills of the applicant (prizes, scholarships, postgraduate research and work activity, etc.) - Up to two names of referees working at universities or national and international research institutions, with details of their e-mail addresses For any further information please contact me. Best regards Pietro --- Pietro Zambelli EURAC Research Institute for Renewable Energy Via G. Di Vittorio 16, I-39100 Bolzano pietro.zambelli @ skype www.eurac.edu

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