Mailman 3 July 2017 - 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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Understanding parameters of scipy.special Mathieu_cem function etc.
by Austin Tobin 27 Jul '17

27 Jul '17

I am trying to relate the Mathieu equation found in Wikipedia Mathieu Equation <https://en.wikipedia.org/wiki/Mathieu_function/> to the one implemented in scipy.special. In Wikipedia there are 3 inputs, a,q and the parameter along which the integration proceeds. mathieu_cem takes three inputs, an order (integer), a characteristic parameter and the the parameter along which the integration proceeds in degrees. I am trying to relate a and q to the order and characteristic parameter. If it helps I am trying to simulate a quadrupole mass spectrometer with a and q being trapping parameters. Regards, Austin

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Re: [SciPy-User] Understanding parameters of scipy.special Mathieu_cem function etc.
by Mark Bakker 26 Jul '17

26 Jul '17

> From: Austin Tobin <austin.tobin(a)gmail.com> > To: scipy-user(a)python.org > Subject: [SciPy-User] Understanding parameters of scipy.special > Mathieu_cem function etc. > Message-ID: > <CAKYpchqjtMLvpvnwy-+X=SUFPhKUKt0XykBk8SFRnz-jr8YSeA@mail. > gmail.com> > Content-Type: text/plain; charset="utf-8" > > I am trying to relate the Mathieu equation found in Wikipedia Mathieu > Equation <https://en.wikipedia.org/wiki/Mathieu_function/> to the one > implemented in scipy.special. In Wikipedia there are 3 inputs, a,q and the > parameter along which the integration proceeds. > > mathieu_cem takes three inputs, an order (integer), a characteristic > parameter and the the parameter along which the integration proceeds in > degrees. I am trying to relate a and q to the order and characteristic > parameter. > > If it helps I am trying to simulate a quadrupole mass spectrometer with a > and q being trapping parameters. > Regards, > Austin > > The wikipedia link is broken. Could you fix that and maybe post some numbers that you are expecting? I am pretty sure the q is the characteristic number, the degrees are the local elliptic coordinates and the order is, well, the order of the function.

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Any least-squares solver handles complex data/coefficients?
by Neal Becker 21 Jul '17

21 Jul '17

Subject says it all

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ANN: Astropy v2.0 released
by Erik Tollerud 07 Jul '17

07 Jul '17

Dear colleagues, We are very happy to announce the v2.0 release of the Astropy package, a core Python package for Astronomy: http://www.astropy.org Astropy is a community-driven Python package intended to contain much of the core functionality and common tools needed for astronomy and astrophysics. New and improved major functionality in this release includes: * Most models now support parameters having units (i.e., being Quantity objects). * A new CCDData class that is directly useful for typical astronomical images and implements the NDData interface. * Coordinate frame objects can now carry proper motions and radial velocities, and will carry them through and transform them between frames. (This functionality is experimental and feedback is greatly desired.) * Many of the typical mixin columns for astropy tables can now be saved into ECSV files and fully round-tripped. * The fft and direct versions of the convolution algorithm in astropy.convolution are now more consistent and work better with typical use cases. * A variety of additions to the astropy.stats subpackage In addition, hundreds of smaller improvements and fixes have been made. An overview of the changes is provided at: http://docs.astropy.org/en/stable/whatsnew/2.0.html Note that the Astropy 2.x series will be the last versions of Astropy that will support Python 2.x. Future versions of Astropy will only support Python 3.x. Instructions for installing Astropy are provided on our website, and extensive documentation can be found at: http://docs.astropy.org If you make use of the Anaconda Python Distribution, you can update to Astropy v2.0 with: conda update astropy If you normally use pip, you can upgrade with: pip install astropy --upgrade Please report any issues, or request new features via our GitHub repository: https://github.com/astropy/astropy/issues Over 232 developers have contributed code to Astropy so far, and you can find out more about the team behind Astropy here: http://www.astropy.org/team.html Astropy v2.0 now repaces v1.0 as the long term support release, and will be supported until the end of 2019. The next major release of Astropy (scheduled for January 2018) will only support Python 3.x. So if you need to use Astropy in a very stable environment in Python 2.7, you should continue to use the 2.0.x series after 3.0.x is released. If you use Astropy directly for your work, or as a dependency to another package, please remember to include the following acknowledgment at the end of papers: “This research made use of Astropy, a community-developed core Python package for Astronomy (Astropy Collaboration, 2013).” where (Astropy Collaboration, 2013) is a reference to the Astropy paper: http://dx.doi.org/10.1051/0004-6361/201322068 Please feel free to forward this announcement to anyone you think might be interested in this release! The announcement can also be found online at http://www.astropy.org/announcements/release-2.0.html. Special thanks to the coordinator for this release: Brigitta Sipocz. Erik Tollerud, Tom Robitaille, Kelle Cruz, and Tom Aldcroft on behalf of The Astropy Collaboration

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NumPy 1.13.1 released
by Charles R Harris 06 Jul '17

06 Jul '17

Hi All, On behalf of the NumPy team, I am pleased to announce the release of NumPy 1.13.1. This is a bugfix release for problems found in 1.13.0. The major changes are: - fixes for the new memory overlap detection, - fixes for the new temporary elision capability, - reversion of the removal of the boolean binary ``-`` operator. It is recommended that users of 1.13.0 upgrade to 1.13.1. Wheels can be found on PyPI <https://pypi.python.org/pypi/numpy>. Source tarballs, zipfiles, release notes, and the changelog are available on github <https://github.com/numpy/numpy/releases/tag/v1.13.1>. Note that the wheels for Python 3.6 are built against 3.6.1, hence will not work when used with 3.6.0 due to Python bug #29943 <https://bugs.python.org/issue29943>. The plan is to release NumPy 1.13.2 shortly after the release of Python 3.6.2 is out with a fix that problem. If you are using 3.6.0, the workaround is to upgrade to 3.6.1 or use an earlier Python version. *Pull requests merged*A total of 19 pull requests were merged for this release. * #9240 DOC: BLD: fix lots of Sphinx warnings/errors. * #9255 Revert "DEP: Raise TypeError for subtract(bool_, bool_)." * #9261 BUG: don't elide into readonly and updateifcopy temporaries for... * #9262 BUG: fix missing keyword rename for common block in numpy.f2py * #9263 BUG: handle resize of 0d array * #9267 DOC: update f2py front page and some doc build metadata. * #9299 BUG: Fix Intel compilation on Unix. * #9317 BUG: fix wrong ndim used in empty where check * #9319 BUG: Make extensions compilable with MinGW on Py2.7 * #9339 BUG: Prevent crash if ufunc doc string is null * #9340 BUG: umath: un-break ufunc where= when no out= is given * #9371 DOC: Add isnat/positive ufunc to documentation * #9372 BUG: Fix error in fromstring function from numpy.core.records... * #9373 BUG: ')' is printed at the end pointer of the buffer in numpy.f2py. * #9374 DOC: Create NumPy 1.13.1 release notes. * #9376 BUG: Prevent hang traversing ufunc userloop linked list * #9377 DOC: Use x1 and x2 in the heaviside docstring. * #9378 DOC: Add $PARAMS to the isnat docstring * #9379 DOC: Update the 1.13.1 release notes *Contributors* A total of 12 people contributed to this release. People with a "+" by their names contributed a patch for the first time. * Andras Deak + * Bob Eldering + * Charles Harris * Daniel Hrisca + * Eric Wieser * Joshua Leahy + * Julian Taylor * Michael Seifert * Pauli Virtanen * Ralf Gommers * Roland Kaufmann * Warren Weckesser

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Graph(?) simplification/optimization problem
by Sebastian Berg 03 Jul '17

03 Jul '17

Hi all, Just in case someone knows simple approaches to this type of problem: I am wondering about finding a nicer solution for something like a graph simplification problem. I have pairs of nodes which are developing/moving over time (pairs if there are two, but at each time step they form a circle with an even number, so calling them "n" and "p"): t=0 -n-------p- | | | | / \ | t=1 -n--p-n--p- \ / | | \/ | | t=2 ------n--p- Now the algorithm is supposed to "remove" the new p at t=1 which is there only for a short time and is assumed to be unimportant. Since you can only remove pairs, one of the "n" needs to be removed as well. In this case, deciding which "n" to also delete does not matter much (there are other heuristics to prefer one direction), but in general it can be non-trivial in my case. The overall goal would be to (also) maximize lifespans for individual "nodes", though. The exact optimization function would probably need some tuning, but I am wondering if there is a nice approach for this type of problem. I imagine that I can formulate it as a big integer programming problem, but it doesn't seem that straight forward on first glance. Currently I use some greedy strategies, but while it works fine, I am pretty sure that in some events it gets stuck and does not find the best solution. Anyway, just in case someone happens to know this type of problem and has a tip :). Regards, Sebastian

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