Mailman 3 March 2018 - 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

LinkedIn ------------ Jelle Feringa requested to add you as a connection on LinkedIn: ------------------------------------------ Jose, I'd like to add you to my professional network on LinkedIn. - Jelle Accept invitation from Jelle Feringa http://www.linkedin.com/e/-3wy1w2-gpkzwpgn-5n/Q6WKH0LACopGJkAw_6fSqajo6R7VM… View invitation from Jelle Feringa http://www.linkedin.com/e/-3wy1w2-gpkzwpgn-5n/Q6WKH0LACopGJkAw_6fSqajo6R7VM… ------------------------------------------ DID YOU KNOW your LinkedIn profile helps you control your public image when people search for you? Setting your profile as public means your LinkedIn profile will come up when people enter your name in leading search engines. Take control of your image! http://www.linkedin.com/e/-3wy1w2-gpkzwpgn-5n/ewp/inv-22/ -- (c) 2011, LinkedIn Corporation

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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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calculating the jacobian for a least-squares problem
by Andrew Nelson 28 Mar '18

28 Mar '18

I would like to calculate the Jacobian for a least squares problem, followed by a Hessian estimation, then the covariance matrix from that Hessian. With my current approach I sometimes experience issues with the covariance matrix in that it's sometimes not positive semi-definite. I am using the covariance matrix to seed a MCMC sampling process by supplying it to `np.random.multivariate_normal` to get initial positions for the MC chain. I am using the following code: ``` from scipy.optimize._numdiff import approx_derivative jac = approx_derivative(residuals_func, x0) hess = np.matmul(jac.T, jac) covar = np.linalg.inv(hess) ``` Note that x0 may not be at a minimum. - would this be the usual way of estimating the Hessian, is there anything incorrect with the approach? - what is the recommended way (i.e. numerically stable) of inverting the Hessian in such a situation? - does `optimize.leastsq` do anything different? - if `x0` is not at a minimum should the covariance matrix be expected to be positive semi-definite anyway?

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instantiating interp1d() is pathologically slow for me, why?
by David Mikolas 26 Mar '18

26 Mar '18

I've just posted this question in SO as well, happy with an answer either place: https://stackoverflow.com/q/49427533/3904031 The following takes over a minute for a few thousand points, whereas it seems it should be taking milliseconds. np.__version__ '1.13.0' scipy.__version__ '0.17.0' https://i.stack.imgur.com/3C69J.png import time import numpy as np import matplotlib.pyplot as plt from scipy.interpolate import interp1d times = [] for n in np.logspace(1, 3.5, 6).astype(int): x = np.arange(n, dtype=float) y = np.vstack((np.cos(x), np.sin(x))) start = time.clock() bob = interp1d(x, y, kind='quadratic', assume_sorted=True) times.append((n, time.clock() - start)) n, tim = zip(*times) plt.figure() plt.plot(n, tim) plt.xscale('log') plt.yscale('log') plt.show()

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Re: [SciPy-User] Error in scipy sparse: multiplication with coo_matrix copy changes the data and the format of the multiplied
by Juan Nunez-Iglesias 25 Mar '18

25 Mar '18

Actually, I hadn’t, and I realised I hadn’t updated scipy to 1.0, so it was a good chance to try both. I’m on macOS. Indeed, on 0.19 I see the same issue you do, while on 1.0 it is gone. Incidentally, there’s a few issues related to this that might have been resolved without closing the corresponding GH issue: https://github.com/scipy/scipy/issues/6902 https://github.com/scipy/scipy/issues/6603 These may have been fixed here: https://github.com/scipy/scipy/issues/7077 https://github.com/scipy/scipy/pull/7078 Any devs want to comment on these? Juan. On 22 Mar 2018, 6:22 PM -0400, Ergun Bicici <ergun.bicici(a)boun.edu.tr>, wrote: > > Hi Juan, > > Ok I am installing: > pip3 install scipy > > How about you? Did you try? > > > Best Regards, > Ergun > > Ergun Biçici > http://bicici.github.com/ > > > > On Thu, Mar 22, 2018 at 11:41 PM, Juan Nunez-Iglesias < > > > > jni.soma(a)gmail.com> wrote: > > > Hi Ergun, > > > > > > You should probably update your SciPy version to 1.0. It is more likely that the “fixed” behaviour reported by David is indeed fixed in 1.0, rather than an OS difference. > > > > > > Juan. > > > > > > On 22 Mar 2018, 10:59 AM -0400, Ergun Bicici <ergun.bicici(a)boun.edu.tr>, wrote: > > > > > > > > I'll wait for response from others. Your environment looks different. Thank you. > > > > > > > > > > > > Best Regards, > > > > Ergun > > > > > > > > Ergun Biçici > > > > http://bicici.github.com/ > > > > > > > > > > > > > On Thu, Mar 22, 2018 at 3:16 PM, David Hagen <david(a)drhagen.com> wrote: > > > > > > > Can you try on linux as well? > > > > > > > > > > > > Sorry, I do not have access to a linux box right now. Can you try 1.0.0 in virtual environment? > > > > > > > > > > > > _______________________________________________ > > > > > > SciPy-User mailing list > > > > > > SciPy-User(a)python.org > > > > > > https://mail.python.org/mailman/listinfo/scipy-user > > > > > > > > > > > > > > _______________________________________________ > > > > SciPy-User mailing list > > > > SciPy-User(a)python.org > > > > https://mail.python.org/mailman/listinfo/scipy-user > > > > > > _______________________________________________ > > > SciPy-User mailing list > > > SciPy-User(a)python.org > > > https://mail.python.org/mailman/listinfo/scipy-user > > > >

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ANN: SciPy 1.0.1 released
by Ralf Gommers 24 Mar '18

24 Mar '18

On behalf of the SciPy development team I am pleased to announce the availability of Scipy 1.0.1. This is a maintenance release, no new features with respect to 1.0.0. See the release notes below for details. Wheels and sources can be found on PyPI (https://pypi.python.org/pypi/scipy) and on Github (https://github.com/scipy/scipy/releases/tag/v1.0.1). The conda-forge channel will be up to date within a couple of hours. Thanks to everyone who contributed to this release! Cheers, Ralf SciPy 1.0.1 Release Notes ==================== SciPy 1.0.1 is a bug-fix release with no new features compared to 1.0.0. Probably the most important change is a fix for an incompatibility between SciPy 1.0.0 and ``numpy.f2py`` in the NumPy master branch. Authors ======= * Saurabh Agarwal + * Alessandro Pietro Bardelli * Philip DeBoer * Ralf Gommers * Matt Haberland * Eric Larson * Denis Laxalde * Mihai Capotă + * Andrew Nelson * Oleksandr Pavlyk * Ilhan Polat * Anant Prakash + * Pauli Virtanen * Warren Weckesser * @xoviat * Ted Ying + A total of 16 people contributed to this release. People with a "+" by their names contributed a patch for the first time. This list of names is automatically generated, and may not be fully complete. Issues closed for 1.0.1 ----------------------- - `#7493 <https://github.com/scipy/scipy/issues/7493>`__: `ndimage.morphology` functions are broken with numpy 1.13.0 - `#8118 <https://github.com/scipy/scipy/issues/8118>`__: minimize_cobyla broken if `disp=True` passed - `#8142 <https://github.com/scipy/scipy/issues/8142>`__: scipy-v1.0.0 pdist with metric=`minkowski` raises `ValueError:... - `#8173 <https://github.com/scipy/scipy/issues/8173>`__: `scipy.stats.ortho_group` produces all negative determinants... - `#8207 <https://github.com/scipy/scipy/issues/8207>`__: gaussian_filter seg faults on float16 numpy arrays - `#8234 <https://github.com/scipy/scipy/issues/8234>`__: `scipy.optimize.linprog` `interior-point` presolve bug with trivial... - `#8243 <https://github.com/scipy/scipy/issues/8243>`__: Make csgraph importable again via `from scipy.sparse import*` - `#8320 <https://github.com/scipy/scipy/issues/8320>`__: scipy.root segfaults with optimizer 'lm' Pull requests for 1.0.1 ----------------------- - `#8068 <https://github.com/scipy/scipy/pull/8068>`__: BUG: fix numpy deprecation test failures - `#8082 <https://github.com/scipy/scipy/pull/8082>`__: BUG: fix solve_lyapunov import - `#8144 <https://github.com/scipy/scipy/pull/8144>`__: MRG: Fix for cobyla - `#8150 <https://github.com/scipy/scipy/pull/8150>`__: MAINT: resolve UPDATEIFCOPY deprecation errors - `#8156 <https://github.com/scipy/scipy/pull/8156>`__: BUG: missing check on minkowski w kwarg - `#8187 <https://github.com/scipy/scipy/pull/8187>`__: BUG: Sign of elements in random orthogonal 2D matrices in "ortho_group_gen"... - `#8197 <https://github.com/scipy/scipy/pull/8197>`__: CI: uninstall oclint - `#8215 <https://github.com/scipy/scipy/pull/8215>`__: Fixes Numpy datatype compatibility issues - `#8237 <https://github.com/scipy/scipy/pull/8237>`__: BUG: optimize: fix bug when variables fixed by bounds are inconsistent... - `#8248 <https://github.com/scipy/scipy/pull/8248>`__: BUG: declare "gfk" variable before call of terminate() in newton-cg - `#8280 <https://github.com/scipy/scipy/pull/8280>`__: REV: reintroduce csgraph import in scipy.sparse - `#8322 <https://github.com/scipy/scipy/pull/8322>`__: MAINT: prevent scipy.optimize.root segfault closes #8320 - `#8334 <https://github.com/scipy/scipy/pull/8334>`__: TST: stats: don't use exact equality check for hdmedian test - `#8477 <https://github.com/scipy/scipy/pull/8477>`__: BUG: signal/signaltools: fix wrong refcounting in PyArray_OrderFilterND - `#8530 <https://github.com/scipy/scipy/pull/8530>`__: BUG: linalg: Fixed typo in flapack.pyf.src. - `#8566 <https://github.com/scipy/scipy/pull/8566>`__: CI: Temporarily pin Cython version to 0.27.3 - `#8573 <https://github.com/scipy/scipy/pull/8573>`__: Backports for 1.0.1 - `#8581 <https://github.com/scipy/scipy/pull/8581>`__: Fix Cython 0.28 build break of qhull.pyx

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Problem of 'rotated' data from mat-file when plotted
by O.Zolotov 23 Mar '18

23 Mar '18

Dear All, I try to ‘plot’ data from mat-file. The matlab file’s URL is https://agupubs.onlinelibrary.wiley.com/action/downloadSupplement?doi=10.10… (~80Mb!) When I plot the figure, it looks rotated over the original one. Is there a way to fix this? I tested it using SciLab also, and it produced the figure as expected, so the problem is unlikely in the file itself. I use the following commands to plot the figure import scipy.io as s import numpy as np ds2 = s.loadmat(r"jgra53413-sup-0003-DataSetS2.mat") jx2, jy2, jz2 = ds2['jx'], ds2['jy'], ds2['jz'] x2, y2, z2 = ds2['x'][0], ds2['y'][0], ds2['z'][0] X2, Y2 = np.meshgrid(x2,y2) import matplotlib.pyplot as plt plt.subplot(2,2,1) plt.pcolor(X2, Y2, jz2[:,:,0]) plt.colorbar() plt.show() The original and plotted images are attached. Regards, Oleg

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[ANN] 11ᵗʰ Advanced Scientific Programming in Python in Camerino, Italy, 3—8 September, 2018
by Python School Organizers 23 Mar '18

23 Mar '18

11ᵗʰ Advanced Scientific Programming in Python ============================================== a Summer School by the G-Node and the University of Camerino https://python.g-node.org Scientists spend more and more time writing, maintaining, and debugging software. While techniques for doing this efficiently have evolved, only few scientists have been trained to use them. As a result, instead of doing their research, they spend far too much time writing deficient code and reinventing the wheel. In this course we will present a selection of advanced programming techniques and best practices which are standard in the industry, but especially tailored to the needs of a programming scientist. Lectures are devised to be interactive and to give the students enough time to acquire direct hands-on experience with the materials. Students will work in pairs throughout the school and will team up to practice the newly learned skills in a real programming project — an entertaining computer game. We use the Python programming language for the entire course. Python works as a simple programming language for beginners, but more importantly, it also works great in scientific simulations and data analysis. We show how clean language design, ease of extensibility, and the great wealth of open source libraries for scientific computing and data visualization are driving Python to become a standard tool for the programming scientist. This school is targeted at Master or PhD students and Post-docs from all areas of science. Competence in Python or in another language such as Java, C/C++, MATLAB, or Mathematica is absolutely required. Basic knowledge of Python and of a version control system such as git, subversion, mercurial, or bazaar is assumed. Participants without any prior experience with Python and/or git should work through the proposed introductory material before the course. We are striving hard to get a pool of students which is international and gender-balanced: see how far we got in previous years <https://python.g-node.org/wiki/archives#stats>! Date & Location =============== 3–8 September, 2018. Camerino, Italy. Application =========== You can apply online: https://python.g-node.org/wiki/applications Application deadline: 23:59 UTC, 31 May, 2018. There will be no deadline extension, so be sure to apply on time. Be sure to read the FAQ before applying: https://python.g-node.org/wiki/faq Participation is for free, i.e. no fee is charged! Participants however should take care of travel, living, and accommodation expenses by themselves. Program ======= • Version control with git and how to contribute to open source projects with GitHub • Best practices in data visualization • Organizing, documenting, and distributing scientific code • Testing scientific code • Profiling scientific code • Advanced NumPy • Advanced scientific Python: decorators, context managers, generators, and elements of object oriented programming • Writing parallel applications in Python • Speeding up scientific code with Cython and numba • Memory-bound computations and the memory hierarchy • Programming in teams Also see the detailed day-by-day schedule: https://python.g-node.org/wiki/schedule Faculty ======= • Ashwin Trikuta Srinath, Cyberinfrastructure Technology Integration, Clemson University, SC USA • Jenni Rinker, Department of Wind Energy, Technical University of Denmark, Roskilde Denmark • Juan Nunez-Iglesias, Melbourne Bioinformatics, University of Melbourne Australia • Nicolas P. Rougier, Inria Bordeaux Sud-Ouest, Institute of Neurodegenerative Disease, University of Bordeaux France • Pietro Berkes, NAGRA Kudelski, Lausanne Switzerland • Rike-Benjamin Schuppner, Institute for Theoretical Biology, Humboldt-Universität zu Berlin Germany • Tiziano Zito, freelance consultant, Berlin Germany • Zbigniew Jędrzejewski-Szmek, Red Hat Inc., Warsaw Poland Organizers ========== For the German Neuroinformatics Node of the INCF (G-Node) Germany: • Tiziano Zito, freelance consultant, Berlin Germany • Caterina Buizza, Personal Robotics Lab, Imperial College London UK • Zbigniew Jędrzejewski-Szmek, Red Hat Inc., Warsaw Poland • Jakob Jordan, Department of Physiology, University of Bern, Switzerland Switzerland For the University of Camerino Italy: • Flavio Corradini, Computer Science Division, School of Science and Technology, University of Camerino Italy • Barbara Re, Computer Science Division, School of Science and Technology, University of Camerino Italy Website: https://python.g-node.org Contact: python-info(a)g-node.org

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Error in scipy sparse: multiplication with coo_matrix copy changes the data and the format of the multiplied
by Ergun Bicici 22 Mar '18

22 Mar '18

After multiplying a coo_matrix from scipy.sparse with its copy, the copy's format changes to column-major and hence its data change while the original data remains the same. Example: import numpy, scipy from scipy.sparse import coo_matrix, csc_matrix a = numpy.random.randn(4,7) print ('a:', a) b = coo_matrix(a) c = b.copy() print ('c before (coo_matrix):', c) print ('c data before (coo_matrix):', c.data) b.multiply(c) print ('c after (coo_matrix):', c) print ('c data after (coo_matrix):', c.data) a: [[ 0.80168251 -1.39944231 0.25345622 1.42371329 -0.12530735 0.50449369 1.07289013] [ 0.45875588 -0.15916679 -0.42632756 0.56484416 -1.46640872 1.42488646 0.03532664] [ 0.72752298 1.34140445 -0.37043453 0.78127267 0.01084105 -2.08957963 -0.10899932] [-0.02555597 -0.87197938 -0.76835675 0.82776032 -2.06292275 -1.2911363 -0.72253431]] c before (coo_matrix): (0, 0) 0.801682509603 (0, 1) -1.39944230956 (0, 2) 0.253456223565 (0, 3) 1.4237132942 (0, 4) -0.125307347426 (0, 5) 0.504493692248 (0, 6) 1.07289013113 (1, 0) 0.458755875333 (1, 1) -0.159166794146 (1, 2) -0.426327564398 (1, 3) 0.564844163879 (1, 4) -1.46640871661 (1, 5) 1.42488646441 (1, 6) 0.0353266410166 (2, 0) 0.727522979291 (2, 1) 1.34140444948 (2, 2) -0.370434529077 (2, 3) 0.781272667369 (2, 4) 0.0108410499861 (2, 5) -2.08957962801 (2, 6) -0.108999316299 (3, 0) -0.0255559745129 (3, 1) -0.871979375733 (3, 2) -0.768356754242 (3, 3) 0.827760318972 (3, 4) -2.0629227477 (3, 5) -1.29113629796 (3, 6) -0.722534309773 c data before (coo_matrix): [ 0.80168251 -1.39944231 0.25345622 1.42371329 -0.12530735 0.50449369 1.07289013 0.45875588 -0.15916679 -0.42632756 0.56484416 -1.46640872 1.42488646 0.03532664 0.72752298 1.34140445 -0.37043453 0.78127267 0.01084105 -2.08957963 -0.10899932 -0.02555597 -0.87197938 -0.76835675 0.82776032 -2.06292275 -1.2911363 -0.72253431] c after (coo_matrix): (0, 0) 0.801682509603 (1, 0) 0.458755875333 (2, 0) 0.727522979291 (3, 0) -0.0255559745129 (0, 1) -1.39944230956 (1, 1) -0.159166794146 (2, 1) 1.34140444948 (3, 1) -0.871979375733 (0, 2) 0.253456223565 (1, 2) -0.426327564398 (2, 2) -0.370434529077 (3, 2) -0.768356754242 (0, 3) 1.4237132942 (1, 3) 0.564844163879 (2, 3) 0.781272667369 (3, 3) 0.827760318972 (0, 4) -0.125307347426 (1, 4) -1.46640871661 (2, 4) 0.0108410499861 (3, 4) -2.0629227477 (0, 5) 0.504493692248 (1, 5) 1.42488646441 (2, 5) -2.08957962801 (3, 5) -1.29113629796 (0, 6) 1.07289013113 (1, 6) 0.0353266410166 (2, 6) -0.108999316299 (3, 6) -0.722534309773 c data after (coo_matrix): [ 0.80168251 0.45875588 0.72752298 -0.02555597 -1.39944231 -0.15916679 1.34140445 -0.87197938 0.25345622 -0.42632756 -0.37043453 -0.76835675 1.42371329 0.56484416 0.78127267 0.82776032 -0.12530735 -1.46640872 0.01084105 -2.06292275 0.50449369 1.42488646 -2.08957963 -1.2911363 1.07289013 0.03532664 -0.10899932 -0.72253431] Best Regards, Ergun Ergun Biçici http://bicici.github.com/ <http://ergunbicici.blogspot.com/>

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