Mailman 3 February 2014 - 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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Normalization for optimization in python
by Yuxiang Wang April 27, 2015

April 27, 2015

Dear all, During optimization, it is often helpful to normalize the input parameters to make them on the same order of magnitude, so the convergence can be much better. For example, if we want to minimize f(x), while a reasonable approximation is x0=[1e3, 1e-4], it might be helpful to normalize x0[0] and x0[1] to about the same order of magnitude (often O(1)). My question is, I have been using scipy.optimize and specifically, the L-BFGS-B algorithm. I was wondering that, do I need to normalize that manually by writing a function, or the algorithm already did it for me? Thank you! -Shawn -- Yuxiang "Shawn" Wang Gerling Research Lab University of Virginia yw5aj(a)virginia.edu +1 (434) 284-0836 https://sites.google.com/a/virginia.edu/yw5aj/

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leastsq and multiprocessing
by Frédéric Parrenin May 30, 2014

May 30, 2014

Dear all, Following these posts: http://stackoverflow.com/questions/10489134/multithreaded-calls-to-the-obje… It seems it is possible to make leastsq take part of multiple processors. I was wondering: given that the tendency of processors is to have more and more cores nowadays, why this is not done by default in leastsq? Best regards, Frédéric Parrenin

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[OT] Job: Postdoctoral Research Associate position at DXARTS
by Joseph Anderson Feb. 28, 2014

Feb. 28, 2014

Hello All, Wanted to let everyone know about a new position at DXARTS / University of Washington (Seattle). Detailed description here (and below): http://ap.washington.edu/ahr/academic-jobs/position/nn0378/ My best, ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Joseph Anderson j.anderson(a)ambisonictoolkit.net http://www.ambisonictoolkit.net ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Position Description: The Center for Digital Arts and Experimental Media (DXARTS) has an outstanding opportunity for a postdoctoral Research Associate (0148) position. The successful candidate will develop tools for a variety of research projects ongoing in our center. These projects encompass the areas of sound synthesis and computer music, experimental video and machine vision, mechatronics and digital fabrication, etc. The position requires the ability to work independently, planning and executing the development of new software, supporting advanced artistic research across many media. Specific Responsibilities Work will involve developing new tools and interfaces for media processing, including real-time systems for sound synthesis and processing, spatial audio, performance technologies, HCI, live video processing tools for immersive installations, sensing and control systems applied to robotics and mechatronic systems, as well as other software tools related to specific research projects ongoing in our center. Travel to national and international venues to support performances and showings of faculty work as well as presentations at conferences and festivals. Requirements: PhD in Computer Science or Digital Arts with focus on media programming for audio, video and physical computing. At least 5 years of programming and software development experience in the following programming languages: C / C++, Objective C, Python, Scheme, Ruby, SuperCollider; experience with standard media libraries such as Open Frameworks, OpenCV, GStreamer, JUCE, etc. The candidate should have experience developing software on the Mac OS X (and iOS) and Linux operating systems and IDEs such as XCode and CodeBlocks and the ability to manage large projects using GIT repositories. Experience with just in time (JIT) compilers such as LLVM would be considered a plus. To apply, please send a cover letter, three letters of reference, a CV, questions about the position and disability accommodations to Billie Grace at bgrace(a)uw.edu. Candidates are encouraged to apply as soon as possible. This is a 100% FTE position. The initial appointment will be for 1 year (12mo. service period), with the option of renewal (up to 6 years). The University of Washington is an affirmative action, equal opportunity employer. The University is building a culturally diverse faculty and staff and strongly encourages applications from women, minorities, individuals with disabilities and covered veterans.

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lfilter produce very low output
by f5owl＠free.fr Feb. 28, 2014

Feb. 28, 2014

Dear list readers, I am new to scipy and I am trying to filter some wav file containing experimental data. The files contain an (almost) sine wave at 5kHz with some 50 and 100Hz noise and other spurious. The fs=44100Hz and samples are 16 bits. I use firwin and lfilter to filter my data between 4000 and 6000 Hz. My problem is that the output of lfilter is very low and seem to be almost independent of the filter frequencies. I have made a test file with a pure 5kHz sine wave and result are consistant. I have to normalize data between -1 and 1 but I have the same problem. I can figure out what is wrong. I put my code and the end of the message. phil1.wav is real data, sin_0_5000_44100.wav is generated sine. Attachment is two plots of the first 400 samples made with the two files. With the simulated sine wave, result is consistent filter delay and low attenuation. With the real data, no filter delay and output very low. Can someone help ? Thank you Here is my code : import matplotlib.pyplot as plt import numpy as np from scipy.signal import lfilter, firwin from scipy.io import wavfile #filename='phil1.wav' filename='sin_0_5000_44100.wav' fs, sig = wavfile.read(filename) # extract first channel r= np.hsplit(sig,2)[0] print('fs = %f'%fs) print('min %f max %f mean %f rms %f'%(r.min(),r.max(),r.mean(),np.sqrt(np.mean(r**2)))) flow=4000 fup=6000 taps = firwin(256,[flow,fup],nyq=fs/2,pass_zero=False, scale=False) rf = lfilter(taps, 1.0, r) print('min %f max %f mean %f rms %f'%(rf.min(),rf.max(),rf.mean(),np.sqrt(np.mean(rf**2)))) t=np.arange(len(r))*(1./fs) plt.subplot(211) plt.title(filename) plt.plot(t[:400],r[:400]) plt.subplot(212) plt.title(filename+' filtered %.0f %.0f'%(flow,fup)) plt.plot(t[:400],rf[:400]) plt.show()

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solve under determined system of eqauation
by nadeesh t v Feb. 27, 2014

Feb. 27, 2014

hi, I have to solve system of equation of the form Ax=B,x>=0 which can be under determined. Which Scipy library function I should use? I know that scipy.optimize.nnls is there but Whether it can solve under determined system of equation? Thanks and Regards

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Re: [SciPy-User] estimating cov_x matrix with leastsq, without doing a fit.
by federico vaggi Feb. 27, 2014

Feb. 27, 2014

To expand on what Joseph was saying: You can take the parameter value that you've obtained at your solution, calculate the Jacobian using finite elements (I didn't know statsmodels had those utility functions, I'll definitely use those in my own code) then use the Jacobian to approximate the Hessian (if you are close to the minima, it's very close to J*J.T) then use the Hessian to calculate the covariance matrix. The only issue with this is that this approach is only really valid if your solution is near a minima already - if it isn't, the approximation for the Hessian might be way off.

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estimating cov_x matrix with leastsq, without doing a fit.
by Andrew Nelson Feb. 27, 2014

Feb. 27, 2014

Dear list, I have a least squares data fitting system. I use two different ways of fitting the data; the first is with a differential evolution algorithm, the second is with the scipy.optimize.leastsq function. When I use the leastsq function I obtain the cov_x matrix, giving me estimated parameter uncertainties. However, with my home rolled DE algorithm I don't get a covariance matrix and wish to estimate one. However, I don't want to change the fitted parameters, I just want the covariance matrix estimated. Is there any way of getting leastsq to give me the covariance matrix solely based on the initial parameters? If there isn't, can anyone suggest the best way for me to accomplish this? At the moment I am using numdifftools to calculate the Hessian and inverting this, but this is not giving me the same numbers as I get out of the leastsq function. regards, Andrew. -- _____________________________________ Dr. Andrew Nelson _____________________________________

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