Mailman 3 December 2015 - 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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ANN: first release candidate for scipy 0.17.0
by Evgeni Burovski 02 Jan '16

02 Jan '16

Hi, I'm pleased to announce the availability of the first release candidate for Scipy 0.17.0. Please try this rc and report any issues on Github tracker or scipy-dev mailing list. Source tarballs and full release notes are available from Github Releases: https://github.com/scipy/scipy/releases/tag/v0.17.0rc1 Please note that this is a source-only release. We do not provide win32 installers for this release. See the email thread starting from <https://mail.scipy.org/pipermail/scipy-dev/2015-December/021103.html> for the rationale and discussion. The updated release schedule is as follows: 10 Jan 2016: rc2 (if needed) 17 Jan 2016: final release. Thanks to everyone who contributed to this release! Evgeni Below is a part of the release notes. ========================== SciPy 0.17.0 Release Notes ========================== .. note:: Scipy 0.17.0 is not released yet! .. contents:: SciPy 0.17.0 is the culmination of 6 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Moreover, our development attention will now shift to bug-fix releases on the 0.17.x branch, and on adding new features on the master branch. This release requires Python 2.6, 2.7 or 3.2-3.5 and NumPy 1.6.2 or greater. Release highlights: - New functions for linear and nonlinear least squares optimization with constraints: `scipy.optimize.lsq_linear` and `scipy.optimize.least_squares` - Support for fitting with bounds in `scipy.optimize.curve_fit`. - Significant improvements to `scipy.stats`, providing many functions with better handing of inputs which have NaNs or are empty, improved documentation, and consistent behavior between `scipy.stats` and `scipy.stats.mstats`. - Significant performance improvements and new functionality in `scipy.spatial.cKDTree`. New features ============ `scipy.cluster` improvements - ---------------------------- A new function `scipy.cluster.hierarchy.cut_tree`, which determines a cut tree from a linkage matrix, was added. `scipy.io` improvements - ----------------------- `scipy.io.mmwrite` gained support for symmetric sparse matrices. `scipy.io.netcdf` gained support for masking and scaling data based on data attributes. `scipy.optimize` improvements - ----------------------------- Linear assignment problem solver ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ `scipy.optimize.linear_sum_assignment` is a new function for solving the linear sum assignment problem. It uses the Hungarian algorithm (Kuhn-Munkres). Least squares optimization ~~~~~~~~~~~~~~~~~~~~~~~~~~ A new function for *nonlinear* least squares optimization with constraints was added: `scipy.optimize.least_squares`. It provides several methods: Levenberg-Marquardt for unconstrained problems, and two trust-region methods for constrained ones. Furthermore it provides different loss functions. New trust-region methods also handle sparse Jacobians. A new function for *linear* least squares optimization with constraints was added: `scipy.optimize.lsq_linear`. It provides a trust-region method as well as an implementation of the Bounded-Variable Least-Squares (BVLS) algorithm. `scipy.optimize.curve_fit` now supports fitting with bounds. `scipy.signal` improvements - -------------------------- A ``mode`` keyword was added to `scipy.signal.spectrogram`, to let it return other spectrograms than power spectral density. `scipy.stats` improvements - -------------------------- Many functions in `scipy.stats` have gained a ``nan_policy`` keyword, which allows specifying how to treat input with NaNs in them: propagate the NaNs, raise an error, or omit the NaNs. Many functions in `scipy.stats` have been improved to correctly handle input arrays that are empty or contain infs/nans. A number of functions with the same name in `scipy.stats` and `scipy.stats.mstats` were changed to have matching signature and behavior. See `gh-5474 <https://github.com/scipy/scipy/issues/5474>`__ for details. `scipy.stats.binom_test` and `scipy.stats.mannwhitneyu` gained a keyword ``alternative``, which allows specifying the hypothesis to test for. Eventually all hypothesis testing functions will get this keyword. For methods of many continuous distributions, complex input is now accepted. Matrix normal distribution has been implemented as `scipy.stats.matrix_normal`. `scipy.sparse` improvements - -------------------------- The `axis` keyword was added to sparse norms, `scipy.sparse.linalg.norm`. `scipy.spatial` improvements - ---------------------------- `scipy.spatial.cKDTree` was partly rewritten for improved performance and several new features were added to it: - - the ``query_ball_point`` method became significantly faster - - ``query`` and ``query_ball_point`` gained an ``n_jobs`` keyword for parallel execution - - build and query methods now release the GIL - - full pickling support - - support for periodic spaces - - the ``sparse_distance_matrix`` method can now return and sparse matrix type `scipy.interpolate` improvements - -------------------------------- Out-of-bounds behavior of `scipy.interpolate.interp1d` has been improved. Use a two-element tuple for the ``fill_value`` argument to specify separate fill values for input above and below the interpolation range. Linear and nearest interpolation kinds of `scipy.interpolate.interp1d` support extrapolation via the ``fill_value="extrapolate"`` keyword. `scipy.linalg` improvements - --------------------------- The default algorithm for `scipy.linalg.leastsq` has been changed to use LAPACK's function ``*gelsd``. Users wanting to get the previous behavior can use a new keyword ``lapack_driver="gelss"`` (allowed values are "gelss", "gelsd" and "gelsy"). ``scipy.sparse`` matrices and linear operators now support the matmul (``@``) operator when available (Python 3.5+). See [PEP 465](http://legacy.python.org/dev/peps/pep-0465/) A new function `scipy.linalg.ordqz`, for QZ decomposition with reordering, has been added. Deprecated features =================== ``scipy.stats.histogram`` is deprecated in favor of ``np.histogram``, which is faster and provides the same functionality. ``scipy.stats.threshold`` and ``scipy.mstats.threshold`` are deprecated in favor of ``np.clip``. See issue #617 for details. ``scipy.stats.ss`` is deprecated. This is a support function, not meant to be exposed to the user. Also, the name is unclear. See issue #663 for details. ``scipy.stats.square_of_sums`` is deprecated. This too is a support function not meant to be exposed to the user. See issues #665 and #663 for details. ``scipy.stats.f_value``, ``scipy.stats.f_value_multivariate``, ``scipy.stats.f_value_wilks_lambda``, and ``scipy.mstats.f_value_wilks_lambda`` are deprecated. These are related to ANOVA, for which ``scipy.stats`` provides quite limited functionality and these functions are not very useful standalone. See issues #660 and #650 for details. ``scipy.stats.chisqprob`` is deprecated. This is an alias. ``stats.chi2.sf`` should be used instead. ``scipy.stats.betai`` is deprecated. This is an alias for ``special.betainc`` which should be used instead. Backwards incompatible changes ============================== The functions ``stats.trim1`` and ``stats.trimboth`` now make sure the elements trimmed are the lowest and/or highest, depending on the case. Slicing without at least partial sorting was previously done, but didn't make sense for unsorted input. When ``variable_names`` is set to an empty list, ``scipy.io.loadmat`` now correctly returns no values instead of all the contents of the MAT file. Element-wise multiplication of sparse matrices now returns a sparse result in all cases. Previously, multiplying a sparse matrix with a dense matrix or array would return a dense matrix. The function ``misc.lena`` has been removed due to license incompatibility. The constructor for ``sparse.coo_matrix`` no longer accepts ``(None, (m,n))`` to construct an all-zero matrix of shape ``(m,n)``. This functionality was deprecated since at least 2007 and was already broken in the previous SciPy release. Use ``coo_matrix((m,n))`` instead. The Cython wrappers in ``linalg.cython_lapack`` for the LAPACK routines ``*gegs``, ``*gegv``, ``*gelsx``, ``*geqpf``, ``*ggsvd``, ``*ggsvp``, ``*lahrd``, ``*latzm``, ``*tzrqf`` have been removed since these routines are not present in the new LAPACK 3.6.0 release. With the exception of the routines ``*ggsvd`` and ``*ggsvp``, these were all deprecated in favor of routines that are currently present in our Cython LAPACK wrappers. Because the LAPACK ``*gegv`` routines were removed in LAPACK 3.6.0. The corresponding Python wrappers in ``scipy.linalg.lapack`` are now deprecated and will be removed in a future release. The source files for these routines have been temporarily included as a part of ``scipy.linalg`` so that SciPy can be built against LAPACK versions that do not provide these deprecated routines. Other changes ============= Html and pdf documentation of development versions of Scipy is now automatically rebuilt after every merged pull request. `scipy.constants` is updated to the CODATA 2014 recommended values. Usage of `scipy.fftpack` functions within Scipy has been changed in such a way that `PyFFTW <http://hgomersall.github.io/pyFFTW/>`__ can easily replace `scipy.fftpack` functions (with improved performance). See `gh-5295 <https://github.com/scipy/scipy/pull/5295>`__ for details. The ``imread`` functions in `scipy.misc` and `scipy.ndimage` were unified, for which a ``mode`` argument was added to `scipy.misc.imread`. Also, bugs for 1-bit and indexed RGB image formats were fixed. ``runtests.py``, the development script to build and test Scipy, now allows building in parallel with ``--parallel``. Authors ======= * @cel4 + * @chemelnucfin + * @endolith * @mamrehn + * @tosh1ki + * Joshua L. Adelman + * Anne Archibald * Hervé Audren + * Vincent Barrielle + * Bruno Beltran + * Sumit Binnani + * Joseph Jon Booker * Olga Botvinnik + * Michael Boyle + * Matthew Brett * Zaz Brown + * Lars Buitinck * Pete Bunch + * Evgeni Burovski * CJ Carey * Ien Cheng + * Cody + * Jaime Fernandez del Rio * Ales Erjavec + * Abraham Escalante * Yves-Rémi Van Eycke + * Yu Feng + * Eric Firing * Francis T. O'Donovan + * André Gaul * Christoph Gohlke * Ralf Gommers * Alex Griffing * Alexander Grigorievskiy * Charles Harris * Jörn Hees + * Ian Henriksen * David Menéndez Hurtado * Gert-Ludwig Ingold * Aakash Jain + * Rohit Jamuar + * Jan Schlüter * Johannes Ballé * Luke Zoltan Kelley + * Jason King + * Andreas Kopecky + * Eric Larson * Denis Laxalde * Antony Lee * Gregory R. Lee * Josh Levy-Kramer + * Sam Lewis + * François Magimel + * Martín Gaitán + * Sam Mason + * Andreas Mayer * Nikolay Mayorov * Damon McDougall + * Robert McGibbon * Sturla Molden * Will Monroe + * Eric Moore * Maniteja Nandana * Vikram Natarajan + * Andrew Nelson * Marti Nito + * Behzad Nouri + * Daisuke Oyama + * Giorgio Patrini + * Fabian Paul + * Christoph Paulik + * Mad Physicist + * Irvin Probst * Sebastian Pucilowski + * Ted Pudlik + * Eric Quintero * Yoav Ram + * Joscha Reimer + * Juha Remes * Frederik Rietdijk + * Rémy Léone + * Christian Sachs + * Skipper Seabold * Sebastian Skoupý + * Alex Seewald + * Andreas Sorge + * Bernardo Sulzbach + * Julian Taylor * Louis Tiao + * Utkarsh Upadhyay + * Jacob Vanderplas * Gael Varoquaux + * Pauli Virtanen * Fredrik Wallner + * Stefan van der Walt * James Webber + * Warren Weckesser * Raphael Wettinger + * Josh Wilson + * Nat Wilson + * Peter Yin + A total of 101 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.

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ANN: PyWavelets 0.4.0
by Gregory Lee 29 Dec '15

29 Dec '15

On behalf of the PyWavelets development team I am pleased to announce the release of PyWavelets 0.4.0. As always, new developers interested in wavelets are welcome to join us at: https://github.com/PyWavelets/pywt Description ----------- PyWavelets is a free Open Source library for wavelet transforms in Python. The main features of PyWavelets are: - 1D, 2D and nD Forward and Inverse Discrete Wavelet Transform (DWT and IDWT) - 1D and 2D Stationary Wavelet Transform (Undecimated Wavelet Transform) - 1D and 2D Wavelet Packet decomposition and reconstruction - Approximating wavelet and scaling functions - Over seventy built-in wavelet filters and custom wavelets supported - Single and double precision calculations - Results compatible with Matlab Wavelet Toolbox (TM) Git (source) repository: https://github.com/PyWavelets/pywt Mailing list: https://groups.google.com/forum/#!forum/pywavelets Documentation: http://pywavelets.readthedocs.org Highlights of this release -------------------------- - 1D and 2D inverse stationary wavelet transforms - Substantially faster 2D and nD discrete wavelet transforms - Complex number support - nD versions of the multilevel DWT and IDWT - modernization/streamlining of the API Full release notes are available here: https://github.com/PyWavelets/pywt/blob/3f4f46a991afc7746bb66ee346af753cb2d… Enjoy, Greg

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fortran unformatted output from python
by Nikhil 28 Dec '15

28 Dec '15

Hi, I am trying to write a binary unformatted sequential output from python using fortranfile.py module. I have managed to figure out most of the code, but I am stuck when I need to use the boolean operation. In fortran file, i need to add some metadata, where I need to provide .false. in the output. When i try o use the struct module to write struct.pack('?',False), it doesnt seem to be recognized by fortran. I was wondering if someone has some suggestions in this regard. Thanks, nikhil

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[ANN-JOB] Project Jupyter is hiring a Project Manager - position at UC Berkeley
by Fernando Perez 22 Dec '15

22 Dec '15

Hi all, [ please direct all replies directly to me ] Project Jupyter is announcing the opening of a position for a full-time project manager, who will help us coordinate our technical development, engage the open source community and work with our multiple stakeholders in academia and industry. If you have experience leading technical teams in open source communities, we'd love to hear from you! In the last few years the project has rapidly grown in multiple directions, and this presents both challenges and opportunities. We are looking for someone who can help us harness the energy and activity from our many contributors that include those funded by our research grants, our industry partners, and the entire open source community. The role of the project manager is to help us maintain this activity focused into a solid whole, so we can deliver timely and robust releases, evolve our architecture coherently, ensure our documentation and communication matches our technical foundation, and continue engaging a wide range of stakeholders to evolve the project in new, interesting and valuable directions. This position will be hosted at the Berkeley Institute for Data Science, working locally with Fernando Perez, Matthias Bussonnier, and our new postdoctoral scholars. But the scope of this role is the entire project, so we are looking for a candidate who will be regularly communicating with project stakeholders from all locations, traveling to conferences, development workshops and other project activities. For specific details on the position and to apply, you can learn more at jobs.berkeley.edu, Job ID #20975: https://hrw-vip-prod.is.berkeley.edu/psc/JOBSPROD/EMPLOYEE/HRMS/c/HRS_HRAM.… Note that while the application review date is listed as January 1, 2016, we will be considering applicants past that date (that is the cutoff for us to be allowed to look at incoming applications). The search will remain open until filled. -- Fernando Perez (@fperez_org; http://fperez.org) fperez.net-at-gmail: mailing lists only (I ignore this when swamped!) fernando.perez-at-berkeley: contact me here for any direct mail

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7th Annual Scientific Software Days Conference (25-26 February, 2016 -- Austin, TX)
by Damon McDougall 18 Dec '15

18 Dec '15

The 7th Annual Scientific Software Days Conference (SSD) <http://scisoftdays.org/> targets users and developers of scientific software. The conference will be held at the University of Texas at Austin Thursday Feb 25 - Friday Feb 26, 2016 and focuses on two themes: a) sharing best practices across scientific software communities; b) sharing the latest tools and technology relevant to scientific software. Past keynotes speakers include Greg Wilson (2008), Victoria Stodden (2009), Steve Easterbrook (2010), Fernando Perez (2011), Will Schroeder (2012), Neil Chue Hong (2013). This year's list of speakers include: - Brian Adams (Sandia, Dakota): http://www.sandia.gov/~briadam/index.html - Iain Dunning (MIT, Julia Project): http://iaindunning.com/ - Victor Eijkhout (TACC): http://pages.tacc.utexas.edu/~eijkhout/ - Robert van de Geijn (keynote, UT Austin, libflame): https://www.cs.utexas.edu/users/rvdg/ - Jeff Hammond (Intel, nwchem): https://jeffhammond.github.io/ - Mark Hoemmen (keynote, Sandia, Trilinos): https://plus.google.com/+MarkHoemmen - James Howison (UT Austin): http://james.howison.name/ - Fernando Perez (Berkeley, IPython): http://fperez.org/ - Cory Quammen (Kitware, Paraview/VTK): http://www.kitware.com/company/team/quammen.html - Ridgway Scott (UChicago, FEniCS): http://people.cs.uchicago.edu/~ridg/ - Roy Stogner (UT Austin, LibMesh): https://scholar.google.com/citations?user=XcurJI0AAAAJ In additional, we solicit poster submissions that share novel uses of scientific software. Please send an abstract of less than 250 words to ssd-organizers(a)googlegroups.com. Limited travel funding for students and early career researchers who present posters will be available. Early-bird registration fees (before Feb 10th): Students: $35 Everyone else: $50 Late registration fees (Feb 10th onwards): Students: $55 Everyone else: $70 More details, including how to register, will appear on the website in the coming weeks: http://scisoftdays.org/ Regards, S. Fomel (UTexas), T. Isaac (UChicago), M. Knepley (Rice), R. Kirby (Baylor), Y. Lai (UTexas), K. Long (Texas Tech), D. McDougall (UTexas), J. Stewart (Sandia)

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Introducing outer/orthonongal indexing to numpy
by Sebastian Berg 18 Dec '15

18 Dec '15

Hello all, sorry for cross posting (discussion should go to the numpy list). But I would like to get a bit of discussion on the introduction of (mostly) two new ways to index numpy arrays. This would also define a way for code working with different array-likes, some of which implement outer indexing (i.e. xray and dask I believe), to avoid ambiguity. The new methods are (names up for discussion): 1. arr.oindex[...] 2. arr.vindex[...] The difference beeing that `oindex` will return outer/orthogonal type indexing, while `vindex` would be a (hopefully) less confusing variant of "fancy" indexing. The biggest reason for introducing this is to provide `oindex` for situations such as: >>> arr = np.arange(25).reshape((5, 5)) >>> arr[[0, 1], [1, 2]] array([1, 7]) >>> # While most might expect the result to be: >>> arr.oindex[[0, 1], [1, 2]] array([[1, 2], [6, 7]]) To provide backwards compatibility the current plan is to also introduce `arr.legacy_index[...]` or similar, with the (long term) plan to force the users to explicitly choose `oindex`, `vindex`, or `legacy_index` if the indexing operation is otherwise not well defined. There are still some open questions for me regarding, for example: * the exact time line (should we start deprecation immediately, etc.) * the handling of boolean indexing arrays * questions that might crop up about other array-likes/subclasses * Are there indexing needs that we are forgetting but are related? More details the current status of my NEP, which has a lot of examples, can be found at: https://github.com/numpy/numpy/pull/6256/files?short_path=01e4dd9#diff-01e4… and comments about are very welcome. There is a fully functional implementation available at https://github.com/numpy/numpy/pull/6075 and you can test it using (after cloning numpy): git fetch upstream pull/6075/head:pr-6075 && git checkout pr-6075; python runtests.py --ipython # Inside ipython (too see the deprecations): import warnings; warnings.simplefilter("always") My current hope for going forward is to get clear feedback of what is wanted, for the naming and generally from third party module people, so that we can polish up the NEP and the community can accept it. With good feedback, I think we may be able to get the new attributes into 1.11. So if you are interested in teaching and have suggestions for the names, or have thoughts about subclasses, or... please share your thoughts! :) Regards, Sebastian

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NumPy 1.10.2 release
by Charles R Harris 14 Dec '15

14 Dec '15

Hi All, I'm pleased to announce the release of Numpy 1.10.2. This release should take care of the bugs discovered in the 1.10.1 release, some of them severe. Upgrading is strongly advised if you are currently using 1.10.1. Windows binaries and source releases can be found at the usual place on Sourceforge <https://sourceforge.net/projects/numpy/files/NumPy/1.10.2/>. The sources are also available from PyPi <https://pypi.python.org/pypi/numpy/>. Cheers, Chuck

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