Mailman 3 January 2018 - 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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optimize a piece of code pruning arrays based on distance
by William Heymann Jan. 24, 2018

Jan. 24, 2018

Hello, [Sorry if this is a duplicate. I realized I sent my previous email from the wrong address] I am trying to find a way to use scipy/numpy to optimize a piece of code. >From profiling I already know over 90% of the runtime of the function is in 4 lines of code. This code is from selSPEA2 in DEAP in case that matters. for i in range(N): for j in range(1, size - 1): if sorted_indices[i,j] == min_pos: sorted_indices[i,j:size - 1] = sorted_indices[i,j + 1:size] sorted_indices[i,j:size] = min_pos break The inner loop is inherently parallel since it is each iteration does not depend on any other. With C++ I would use OpenMP or TBB to parallelize the outer loop. For a more complete picture I have also included the surrounding code. The basic problem is if the number of entries (size) is greater than the number allowed to be kept (k) for the next generation then the most similar items (based on distance) need to be removed until size == k. At each iteration a solution with the lowest distance to another solution is removed and then the distance from the remove solution to all other solutions is set as infinity. This prevents removing all points in a cluster. Thank you while size > k: # Search for minimal distance min_pos = 0 for i in range(1, N): for j in range(1, size): dist_i_sorted_j = distances[i,sorted_indices[i,j]] dist_min_sorted_j = distances[min_pos,sorted_indices[min_pos,j]] if dist_i_sorted_j < dist_min_sorted_j: min_pos = i break elif dist_i_sorted_j > dist_min_sorted_j: break distances[:,min_pos] = float("inf") distances[min_pos,:] = float("inf") #This part is still expensive but I don't know a better way to do it yet. #Essentially all remaining time in this function is in this section #It may even make sense to do this in C++ later since it is trivially parallel for i in range(N): for j in range(1, size - 1): if sorted_indices[i,j] == min_pos: sorted_indices[i,j:size - 1] = sorted_indices[i,j + 1:size] sorted_indices[i,j:size] = min_pos break # Remove corresponding individual from chosen_indices to_remove.append(min_pos) size -= 1

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Can fftconvolve use a faster fft?
by Neal Becker Jan. 13, 2018

Jan. 13, 2018

Can fftconvolve use fftw, or mkl fft? (Sorry if this post is a dup, I tried posting via gmane but I don't think it's working)

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NumPy 1.14.0 release
by Charles R Harris Jan. 7, 2018

Jan. 7, 2018

Hi All, On behalf of the NumPy team, I am pleased to announce NumPy 1.14.0. Numpy 1.14.0 is the result of seven months of work and contains a large number of bug fixes and new features, along with several changes with potential compatibility issues. The major change that users will notice are the stylistic changes in the way numpy arrays and scalars are printed, a change that will affect doctests. See the release notes for details on how to preserve the old style printing when needed. A major decision affecting future development concerns the schedule for dropping Python 2.7 support in the runup to 2020. The decision has been made to support 2.7 for all releases made in 2018, with the last release being designated a long term release with support for bug fixes extending through the end of 2019. Starting from January, 2019 support for 2.7 will be dropped in all new releases. More details can be found in the relevant NEP <https://github.com/numpy/numpy/blob/master/doc/neps/dropping-python2.7-prop…> . This release supports Python 2.7 and 3.4 - 3.6. Wheels for the release are available on PyPI. Source tarballs, zipfiles, release notes, and the changelog are available on github <https://github.com/numpy/numpy/releases/tag/v1.14.0>. *Highlights* - The ``np.einsum`` function uses BLAS when possible - ``genfromtxt``, ``loadtxt``, ``fromregex`` and ``savetxt`` can now handle files with arbitrary Python supported encoding. - Major improvements to printing of NumPy arrays and scalars. *New functions* - ``parametrize``: decorator added to numpy.testing - ``chebinterpolate``: Interpolate function at Chebyshev points. - ``format_float_positional`` and ``format_float_scientific`` : format floating-point scalars unambiguously with control of rounding and padding. - ``PyArray_ResolveWritebackIfCopy`` and ``PyArray_SetWritebackIfCopyBase``, new C-API functions useful in achieving PyPy compatibity. *Contributors* A total of 100 people contributed to this release. People with a "+" by their names contributed a patch for the first time. * Alexey Brodkin + * Allan Haldane * Andras Deak + * Andrew Lawson + * Anna Chiara + * Antoine Pitrou * Bernhard M. Wiedemann + * Bob Eldering + * Brandon Carter * CJ Carey * Charles Harris * Chris Lamb * Christoph Boeddeker + * Christoph Gohlke * Daniel Hrisca + * Daniel Smith * Danny Hermes * David Freese * David Hagen * David Linke + * David Schaefer + * Dillon Niederhut + * Egor Panfilov + * Emilien Kofman * Eric Wieser * Erik Bray + * Erik Quaeghebeur + * Garry Polley + * Gunjan + * Han Shen + * Henke Adolfsson + * Hidehiro NAGAOKA + * Hemil Desai + * Hong Xu + * Iryna Shcherbina + * Jaime Fernandez * James Bourbeau + * Jamie Townsend + * Jarrod Millman * Jean Helie + * Jeroen Demeyer + * John Goetz + * John Kirkham * John Zwinck * Jonathan Helmus * Joseph Fox-Rabinovitz * Joseph Paul Cohen + * Joshua Leahy + * Julian Taylor * Jörg Döpfert + * Keno Goertz + * Kevin Sheppard + * Kexuan Sun + * Konrad Kapp + * Kristofor Maynard + * Licht Takeuchi + * Loïc Estève * Lukas Mericle + * Marten van Kerkwijk * Matheus Portela + * Matthew Brett * Matti Picus * Michael Lamparski + * Michael Odintsov + * Michael Schnaitter + * Michael Seifert * Mike Nolta * Nathaniel J. Smith * Nelle Varoquaux + * Nicholas Del Grosso + * Nico Schlömer + * Oleg Zabluda + * Oleksandr Pavlyk * Pauli Virtanen * Pim de Haan + * Ralf Gommers * Robert T. McGibbon + * Roland Kaufmann * Sebastian Berg * Serhiy Storchaka + * Shitian Ni + * Spencer Hill + * Srinivas Reddy Thatiparthy + * Stefan Winkler + * Stephan Hoyer * Steven Maude + * SuperBo + * Thomas Köppe + * Toon Verstraelen * Vedant Misra + * Warren Weckesser * Wirawan Purwanto + * Yang Li + * Ziyan Zhou + * chaoyu3 + * orbit-stabilizer + * solarjoe * wufangjie + * xoviat + * Élie Gouzien + Cheers, Charles Harris

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