Mailman 3 March 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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Connect Input File Pysces
by Syry April 1, 2014

April 1, 2014

Hi! I'm using Pysces to reproduce the cell metabolism. I want to say if is possible to connect the Pysces Input File. I need to connect the input file beacause the output of the first input file is the input of the second. There is anyone that can help me? Thanks

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resample and aggregate a numpy array
by Pietro March 27, 2014

March 27, 2014

Dear All, I would like to resample a numpy array using a function to aggregate the subtiles. I try to clarify the problem with a figure: # defineed an array, like: [[1, 1, 2, 2, 3], [1, 1, 2, 2, 3], [1, 1, 2, 2, 3], [1, 1, 2, 2, 3], [1, 1, 2, 2, 3]] # I would like to split, like: +------+------+------+ | 1, 1 | 2, 2 | 3 | | 1, 1 | 2, 2 | 3 | +------+------+------+ | 1, 1 | 2, 2 | 3 | | 1, 1 | 2, 2 | 3 | +------+------+------+ | 1, 1 | 2, 2 | 3 | | | | | +------+------+------+ # and aggregate using a function like: np.sum, to get: [[4, 8, 6], [4, 8, 6], [2, 4, 3]] I wrote two functions: resample_array that reshape the 2D array into a 3D array, and then I can apply whatever function along axis=2; resample that takes directly the function to be more efficient from a memory point. The code is available here: https://gist.github.com/zarch/9782938 Do you think that there is any better/efficient/faster approach? I’ve looked on numpy/scipy functions but I was not able to find anything to do this kind of things, have I missed something? All the best! Pietro

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scipy.linalg.blas before 0.12.0
by Skipper Seabold March 26, 2014

March 26, 2014

Hi, More feeling around in the dark. The docs [1] state that the low-level functions scipy.linalg.blas are new in 0.12.0. However, >>> import scipy as sp >>> sp.__version__ '0.9.0' >>> import scipy.linalg.blas >>> scipy.linalg.blas.cblas.dgemm._cpointer <PyCObject object at 0x2a94788> What am I missing? Is this just my build and it's not guaranteed? Can I still reliably do things like this [2,3] with scipy < 0.12.0? Skipper [1] http://docs.scipy.org/doc/scipy/reference/linalg.blas.html [2] http://www.mail-archive.com/numpy-discussion@scipy.org/msg40554.html [3] https://github.com/ChadFulton/pykalman_filter/blob/master/kalman/kalman_fil…

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Re: [SciPy-User] [Numpy-discussion] ANN: NumPy 1.8.1 release
by Matthew Brett March 26, 2014

March 26, 2014

Hi, On Tue, Mar 25, 2014 at 4:38 PM, Julian Taylor <jtaylor.debian(a)googlemail.com> wrote: > Hello, > > I'm happy to announce the of Numpy 1.8.1. > This is a bugfix only release supporting Python 2.6 - 2.7 and 3.2 - 3.4. > > More than 48 issues have been fixed, the most important issues are > listed in the release notes: > https://github.com/numpy/numpy/blob/maintenance/1.8.x/doc/release/1.8.1-not… > > Compared to the last release candidate we have fixed a regression of the > 1.8 series that prevented using some gufunc based linalg functions on > larger matrices on 32 bit systems. This implied a few changes in the > NDIter C-API which might expose insufficient checks for error conditions > in third party applications. Please check the release notes for details. > > Source tarballs, windows installers and release notes can be found at > https://sourceforge.net/projects/numpy/files/NumPy/1.8.1 Thanks a lot for this. I've just posted OSX wheels for Pythons 2.7, 3.3, 3.4. It's a strange feeling doing this: $ pip install numpy Downloading/unpacking numpy Downloading numpy-1.8.1-cp27-none-macosx_10_6_intel.whl (3.6MB): 3.6MB downloaded Installing collected packages: numpy Successfully installed numpy Cleaning up... 5 seconds waiting on a home internet connection and a numpy install.... Nice. Cheers, Matthew

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SVG logo of SciPy
by Christophe Bal March 21, 2014

March 21, 2014

Hello, is there a SVG version of the SciPy logos ? This would be to be used on my website. Christophe BAL

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