Mailman 3 September 2017 - SciPy-User

How to display a RGB image (MFISH)?
by jp 03 Apr '23

03 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

3 4

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

2 1

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

5 5

NumPy 1.13.3 released.
by Charles R Harris 30 Sep '17

30 Sep '17

HI All, On behalf of the NumPy team, I am pleased to annouce the release of Numpy 1.13.3. This is a re-release of 1.13.2, which suffered from compatibility problems, see issue 9786 <https://github.com/numpy/numpy/issues/9786>. It is a bugfix release for some problems found since 1.13.1. The most important fixes are for CVE-2017-12852 and the new temporary elision. Users of earlier versions of 1.13 should upgrade. The Python versions supported are 2.7 and 3.4 - 3.6. The Python 3.6 wheels available from PIP are built with Python 3.6.2 and should be compatible with all previous versions of Python 3.6. It was cythonized with Cython 0.26.1, which should be free of the bugs found in 0.27 while also being compatible with Python 3.7-dev. The Windows wheels were built with OpenBlas instead ATLAS, which should improve the performance of the linear algebra functions. Wheels and zip archives are available from PyPI <https://pypi.python.org/pypi?:action=files&name=numpy&version=1.13.3>, both zip and tar archives are available from Github <https://github.com/numpy/numpy/releases/tag/v1.13.3>. Contributors ============ A total of 12 people contributed to this release. People with a "+" by their names contributed a patch for the first time. * Allan Haldane * Brandon Carter * Charles Harris * Eric Wieser * Iryna Shcherbina + * James Bourbeau + * Jonathan Helmus * Julian Taylor * Matti Picus * Michael Lamparski + * Michael Seifert * Ralf Gommers Pull requests merged ==================== A total of 20 pull requests were merged for this release. * #9390 BUG: Return the poly1d coefficients array directly * #9555 BUG: Fix regression in 1.13.x in distutils.mingw32ccompiler. * #9556 BUG: Fix true_divide when dtype=np.float64 specified. * #9557 DOC: Fix some rst markup in numpy/doc/basics.py. * #9558 BLD: Remove -xhost flag from IntelFCompiler. * #9559 DOC: Removes broken docstring example (source code, png, pdf)... * #9580 BUG: Add hypot and cabs functions to WIN32 blacklist. * #9732 BUG: Make scalar function elision check if temp is writeable. * #9736 BUG: Various fixes to np.gradient * #9742 BUG: Fix np.pad for CVE-2017-12852 * #9744 BUG: Check for exception in sort functions, add tests * #9745 DOC: Add whitespace after "versionadded::" directive so it actually... * #9746 BUG: Memory leak in np.dot of size 0 * #9747 BUG: Adjust gfortran version search regex * #9757 BUG: Cython 0.27 breaks NumPy on Python 3. * #9764 BUG: Ensure `_npy_scaled_cexp{,f,l}` is defined when needed. * #9765 BUG: PyArray_CountNonzero does not check for exceptions * #9766 BUG: Fixes histogram monotonicity check for unsigned bin values * #9767 BUG: Ensure consistent result dtype of count_nonzero * #9771 BUG, MAINT: Fix mtrand for Cython 0.27. Enjoy Chuck

1 0

Re: [SciPy-User] ANN: SciPy 1.0 beta release
by Joe Harrington 29 Sep '17

29 Sep '17

On Sun, 17 Sep 2017 22:48:35 +1200 Ralf Gommers <ralf.gommers(a)gmail.com> wrote: > I'm excited to be able to announce the availability of the first beta > release of Scipy 1.0. > [I won't quote the entire release notes!] Awesome news, looooong in the making! The number of deprecations and removals is understandably large. I think we need a centralized resource for navigating deprecations, removals, and changes. It could be a simple web page, or a more involved database. It should list the change, give or link to the rationale, and state the date and package version number. Most importantly, it should give the suggested replacement(s). Googling "scipy deprecation" found only this page, plus a bunch of release notes and discussions. The page was last updated on Jan 16, 2017, and is limited to C-API deprecations: https://docs.scipy.org/doc/numpy-dev/reference/c-api.deprecations.html Even once such a document/page/searchable database exists, many will be unaware of it, or not know where to find it, so referring to it at the top of any list of deprecations/removals would be good. Once it exists, deprecations could be entered before being implemented, and deprecation warnings could refer to an index number in this resource, where the user could go for more information on how to fix the issue. Thanks, --jh--

5 10

order_filter
by Neal Becker 28 Sep '17

28 Sep '17

Is there a description of what is "order_filter"? https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.order_fil… Googling for it is almost impossible (unless you want to order filters from amazon)

2 1

NumPy 1.13.2 released.
by Charles R Harris 28 Sep '17

28 Sep '17

HI All, On behalf of the NumPy team, I am pleased to annouce the release of Numpy 1.13.2. This is a bugfix release for some problems found since 1.13.1. The most important fixes are for CVE-2017-12852 and temporary elision. Users of earlier versions of 1.13 should upgrade. The Python versions supported are 2.7 and 3.4 - 3.6. The Python 3.6 wheels available from PIP are built with Python 3.6.2 and should be compatible with all previous versions of Python 3.6. The Windows wheels are now built with OpenBlas instead ATLAS, which should improve the performance of the linearalgebra functions. Contributors ============ A total of 12 people contributed to this release. People with a "+" by their names contributed a patch for the first time. * Allan Haldane * Brandon Carter * Charles Harris * Eric Wieser * Iryna Shcherbina + * James Bourbeau + * Jonathan Helmus * Julian Taylor * Matti Picus * Michael Lamparski + * Michael Seifert * Ralf Gommers Pull requests merged ==================== A total of 20 pull requests were merged for this release. * #9390 BUG: Return the poly1d coefficients array directly * #9555 BUG: Fix regression in 1.13.x in distutils.mingw32ccompiler. * #9556 BUG: Fix true_divide when dtype=np.float64 specified. * #9557 DOC: Fix some rst markup in numpy/doc/basics.py. * #9558 BLD: Remove -xhost flag from IntelFCompiler. * #9559 DOC: Removes broken docstring example (source code, png, pdf)... * #9580 BUG: Add hypot and cabs functions to WIN32 blacklist. * #9732 BUG: Make scalar function elision check if temp is writeable. * #9736 BUG: Various fixes to np.gradient * #9742 BUG: Fix np.pad for CVE-2017-12852 * #9744 BUG: Check for exception in sort functions, add tests * #9745 DOC: Add whitespace after "versionadded::" directive so it actually... * #9746 BUG: Memory leak in np.dot of size 0 * #9747 BUG: Adjust gfortran version search regex * #9757 BUG: Cython 0.27 breaks NumPy on Python 3. * #9764 BUG: Ensure `_npy_scaled_cexp{,f,l}` is defined when needed. * #9765 BUG: PyArray_CountNonzero does not check for exceptions * #9766 BUG: Fixes histogram monotonicity check for unsigned bin values * #9767 BUG: Ensure consistent result dtype of count_nonzero * #9771 BUG, MAINT: Fix mtrand for Cython 0.27. Enjoy Chuck

1 0

ANN: first SciPy 1.0.0 release candidate
by Ralf Gommers 28 Sep '17

28 Sep '17

Hi all, I'm excited to be able to announce the availability of the first release candidate of Scipy 1.0. This is a big release, and a version number that has been 16 years in the making. It contains a few more deprecations and backwards incompatible changes than an average release. Therefore please do test it on your own code, and report any issues on the Github issue tracker or on the scipy-dev mailing list. Sources and binary wheels can be found at https://pypi.python.org/pypi/scipy and https://github.com/scipy/scipy/releases/tag/v1.0.0rc1. To install with pip: pip install --pre --upgrade scipy Thanks to everyone who contributed to this release! Ralf Pull requests merged after v1.0.0b1: - `#7876 <https://github.com/scipy/scipy/pull/7876>`__: GEN: Add comments to the tests for clarification - `#7891 <https://github.com/scipy/scipy/pull/7891>`__: ENH: backport #7879 to 1.0.x - `#7902 <https://github.com/scipy/scipy/pull/7902>`__: MAINT: signal: Make freqz handling of multidim. arrays match... - `#7905 <https://github.com/scipy/scipy/pull/7905>`__: REV: restore wminkowski - `#7908 <https://github.com/scipy/scipy/pull/7908>`__: FIX: Avoid bad ``__del__`` (close) behavior - `#7918 <https://github.com/scipy/scipy/pull/7918>`__: TST: mark two optimize.linprog tests as xfail. See gh-7877. - `#7929 <https://github.com/scipy/scipy/pull/7929>`__: MAINT: changed defaults to lower in sytf2, sytrf and hetrf - `#7938 <https://github.com/scipy/scipy/pull/7938>`__: MAINT: backports from 1.0.x - `#7939 <https://github.com/scipy/scipy/pull/7939>`__: Fix umfpack solver construction for win-amd64 ========================== SciPy 1.0.0 Release Notes ========================== .. note:: Scipy 1.0.0 is not released yet! .. contents:: SciPy 1.0.0 is the culmination of 8 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 1.0.x branch, and on adding new features on the master branch. Some of the highlights of this release are: - Major build improvements. Windows wheels are available on PyPI for the first time, and continuous integration has been set up on Windows and OS X in addition to Linux. - A set of new ODE solvers and a unified interface to them (`scipy.integrate.solve_ivp`). - Two new trust region optimizers and a new linear programming method, with improved performance compared to what `scipy.optimize` offered previously. - Many new BLAS and LAPACK functions were wrapped. The BLAS wrappers are now complete. This release requires Python 2.7 or 3.4+ and NumPy 1.8.2 or greater. This is also the last release to support LAPACK 3.1.x - 3.3.x. Moving the lowest supported LAPACK version to >3.2.x was long blocked by Apple Accelerate providing the LAPACK 3.2.1 API. We have decided that it's time to either drop Accelerate or, if there is enough interest, provide shims for functions added in more recent LAPACK versions so it can still be used. New features ============ `scipy.cluster` improvements ---------------------------- `scipy.cluster.hierarchy.optimal_leaf_ordering`, a function to reorder a linkage matrix to minimize distances between adjacent leaves, was added. `scipy.fftpack` improvements ---------------------------- N-dimensional versions of the discrete sine and cosine transforms and their inverses were added as ``dctn``, ``idctn``, ``dstn`` and ``idstn``. `scipy.integrate` improvements ------------------------------ A set of new ODE solvers have been added to `scipy.integrate`. The convenience function `scipy.integrate.solve_ivp` allows uniform access to all solvers. The individual solvers (``RK23``, ``RK45``, ``Radau``, ``BDF`` and ``LSODA``) can also be used directly. `scipy.linalg` improvements ---------------------------- The BLAS wrappers in `scipy.linalg.blas` have been completed. Added functions are ``*gbmv``, ``*hbmv``, ``*hpmv``, ``*hpr``, ``*hpr2``, ``*spmv``, ``*spr``, ``*tbmv``, ``*tbsv``, ``*tpmv``, ``*tpsv``, ``*trsm``, ``*trsv``, ``*sbmv``, ``*spr2``, Wrappers for the LAPACK functions ``*gels``, ``*stev``, ``*sytrd``, ``*hetrd``, ``*sytf2``, ``*hetrf``, ``*sytrf``, ``*sycon``, ``*hecon``, ``*gglse``, ``*stebz``, ``*stemr``, ``*sterf``, and ``*stein`` have been added. The function `scipy.linalg.subspace_angles` has been added to compute the subspace angles between two matrices. The function `scipy.linalg.clarkson_woodruff_transform` has been added. It finds low-rank matrix approximation via the Clarkson-Woodruff Transform. The functions `scipy.linalg.eigh_tridiagonal` and `scipy.linalg.eigvalsh_tridiagonal`, which find the eigenvalues and eigenvectors of tridiagonal hermitian/symmetric matrices, were added. `scipy.ndimage` improvements ---------------------------- Support for homogeneous coordinate transforms has been added to `scipy.ndimage.affine_transform`. The ``ndimage`` C code underwent a significant refactoring, and is now a lot easier to understand and maintain. `scipy.optimize` improvements ----------------------------- The methods ``trust-region-exact`` and ``trust-krylov`` have been added to the function `scipy.optimize.minimize`. These new trust-region methods solve the subproblem with higher accuracy at the cost of more Hessian factorizations (compared to dogleg) or more matrix vector products (compared to ncg) but usually require less nonlinear iterations and are able to deal with indefinite Hessians. They seem very competitive against the other Newton methods implemented in scipy. `scipy.optimize.linprog` gained an interior point method. Its performance is superior (both in accuracy and speed) to the older simplex method. `scipy.signal` improvements --------------------------- An argument ``fs`` (sampling frequency) was added to the following functions: ``firwin``, ``firwin2``, ``firls``, and ``remez``. This makes these functions consistent with many other functions in `scipy.signal` in which the sampling frequency can be specified. `scipy.signal.freqz` has been sped up significantly for FIR filters. `scipy.sparse` improvements --------------------------- Iterating over and slicing of CSC and CSR matrices is now faster by up to ~35%. The ``tocsr`` method of COO matrices is now several times faster. The ``diagonal`` method of sparse matrices now takes a parameter, indicating which diagonal to return. `scipy.sparse.linalg` improvements ---------------------------------- A new iterative solver for large-scale nonsymmetric sparse linear systems, `scipy.sparse.linalg.gcrotmk`, was added. It implements ``GCROT(m,k)``, a flexible variant of ``GCROT``. `scipy.sparse.linalg.lsmr` now accepts an initial guess, yielding potentially faster convergence. SuperLU was updated to version 5.2.1. `scipy.spatial` improvements ---------------------------- Many distance metrics in `scipy.spatial.distance` gained support for weights. The signatures of `scipy.spatial.distance.pdist` and `scipy.spatial.distance.cdist` were changed to ``*args, **kwargs`` in order to support a wider range of metrics (e.g. string-based metrics that need extra keywords). Also, an optional ``out`` parameter was added to ``pdist`` and ``cdist`` allowing the user to specify where the resulting distance matrix is to be stored `scipy.stats` improvements -------------------------- The methods ``cdf`` and ``logcdf`` were added to `scipy.stats.multivariate_normal`, providing the cumulative distribution function of the multivariate normal distribution. New statistical distance functions were added, namely `scipy.stats.wasserstein_distance` for the first Wasserstein distance and `scipy.stats.energy_distance` for the energy distance. Deprecated features =================== The following functions in `scipy.misc` are deprecated: ``bytescale``, ``fromimage``, ``imfilter``, ``imread``, ``imresize``, ``imrotate``, ``imsave``, ``imshow`` and ``toimage``. Most of those functions have unexpected behavior (like rescaling and type casting image data without the user asking for that). Other functions simply have better alternatives. ``scipy.interpolate.interpolate_wrapper`` and all functions in that submodule are deprecated. This was a never finished set of wrapper functions which is not relevant anymore. The ``fillvalue`` of `scipy.signal.convolve2d` will be cast directly to the dtypes of the input arrays in the future and checked that it is a scalar or an array with a single element. ``scipy.spatial.distance.matching`` is deprecated. It is an alias of `scipy.spatial.distance.hamming`, which should be used instead. Implementation of `scipy.spatial.distance.wminkowski` was based on a wrong interpretation of the metric definition. In scipy 1.0 it has been just deprecated in the documentation to keep retro-compatibility but is recommended to use the new version of `scipy.spatial.distance.minkowski` that implements the correct behaviour. Positional arguments of `scipy.spatial.distance.pdist` and `scipy.spatial.distance.cdist` should be replaced with their keyword version. Backwards incompatible changes ============================== The following deprecated functions have been removed from `scipy.stats`: ``betai``, ``chisqprob``, ``f_value``, ``histogram``, ``histogram2``, ``pdf_fromgamma``, ``signaltonoise``, ``square_of_sums``, ``ss`` and ``threshold``. The following deprecated functions have been removed from `scipy.stats.mstats`: ``betai``, ``f_value_wilks_lambda``, ``signaltonoise`` and ``threshold``. The deprecated ``a`` and ``reta`` keywords have been removed from `scipy.stats.shapiro`. The deprecated functions ``sparse.csgraph.cs_graph_components`` and ``sparse.linalg.symeig`` have been removed from `scipy.sparse`. The following deprecated keywords have been removed in `scipy.sparse.linalg`: ``drop_tol`` from ``splu``, and ``xtype`` from ``bicg``, ``bicgstab``, ``cg``, ``cgs``, ``gmres``, ``qmr`` and ``minres``. The deprecated functions ``expm2`` and ``expm3`` have been removed from `scipy.linalg`. The deprecated keyword ``q`` was removed from `scipy.linalg.expm`. And the deprecated submodule ``linalg.calc_lwork`` was removed. The deprecated functions ``C2K``, ``K2C``, ``F2C``, ``C2F``, ``F2K`` and ``K2F`` have been removed from `scipy.constants`. The deprecated ``ppform`` class was removed from `scipy.interpolate`. The deprecated keyword ``iprint`` was removed from `scipy.optimize.fmin_cobyla`. The default value for the ``zero_phase`` keyword of `scipy.signal.decimate` has been changed to True. The ``kmeans`` and ``kmeans2`` functions in `scipy.cluster.vq` changed the method used for random initialization, so using a fixed random seed will not necessarily produce the same results as in previous versions. `scipy.special.gammaln` does not accept complex arguments anymore. The deprecated functions ``sph_jn``, ``sph_yn``, ``sph_jnyn``, ``sph_in``, ``sph_kn``, and ``sph_inkn`` have been removed. Users should instead use the functions ``spherical_jn``, ``spherical_yn``, ``spherical_in``, and ``spherical_kn``. Be aware that the new functions have different signatures. The cross-class properties of `scipy.signal.lti` systems have been removed. The following properties/setters have been removed: Name - (accessing/setting has been removed) - (setting has been removed) * StateSpace - (``num``, ``den``, ``gain``) - (``zeros``, ``poles``) * TransferFunction (``A``, ``B``, ``C``, ``D``, ``gain``) - (``zeros``, ``poles``) * ZerosPolesGain (``A``, ``B``, ``C``, ``D``, ``num``, ``den``) - () ``signal.freqz(b, a)`` with ``b`` or ``a`` >1-D raises a ``ValueError``. This was a corner case for which it was unclear that the behavior was well-defined. The method ``var`` of `scipy.stats.dirichlet` now returns a scalar rather than an ndarray when the length of alpha is 1. Other changes ============= SciPy now has a formal governance structure. It consists of a BDFL (Pauli Virtanen) and a Steering Committee. See `the governance document < https://github.com/scipy/scipy/blob/master/doc/source/dev/governance/govern… >`_ for details. It is now possible to build SciPy on Windows with MSVC + gfortran! Continuous integration has been set up for this build configuration on Appveyor, building against OpenBLAS. Continuous integration for OS X has been set up on TravisCI. The SciPy test suite has been migrated from ``nose`` to ``pytest``. ``scipy/_distributor_init.py`` was added to allow redistributors of SciPy to add custom code that needs to run when importing SciPy (e.g. checks for hardware, DLL search paths, etc.). Support for PEP 518 (specifying build system requirements) was added - see ``pyproject.toml`` in the root of the SciPy repository. In order to have consistent function names, the function ``scipy.linalg.solve_lyapunov`` is renamed to `scipy.linalg.solve_continuous_lyapunov`. The old name is kept for backwards-compatibility.

2 1

Diagonalization of a tridiagonal, symmetric, sparse matrix with scipy.sparse.linalg.eigsh()
by Simone Bolognini 22 Sep '17

22 Sep '17

Hi everyone! Here's my problem. I have an NxN symmetric and tridiagonal matrix computed by a Python code and I want to diagonalize it. In the specific case I'm dealing with N = 6000, but the matrix can become larger. Since it is sparse, I assumed the best way to diagonalize it was to use the algorithm scipy.sparse.linalg.eigsh(), which performed extremely good with other sparse and symmetric matrices (not tridiagonal ones, though) I worked with. In particular, since I need only the low lying part of the spectrum, I'm specifying k=2 and which='SM' in the function. However, in this case this algorithm seems not to work, since after approximately 20 minutes of computation I get the following error: ArpackNoConvergence: ARPACK error -1: No convergence (60001 iterations, 0/2 eigenvectors converged) Why is this happening? Is it a problem related to some properties of tridiagonal matrices? What other scipy routine can I use in order to diagonalize my matrix in an efficient way? Here's a minimal code to reproduce my error: import scipy.sparse.linalg as slimport numpy as np dim = 6000 a = np.empty( dim - 1 ) a.fill( 1. ) diag_up = np.diag( a, 1 ) diag_bot = np.diag( a, -1 ) b = np.empty( dim ) b.fill( 1. ) mat = np.diag( b ) + diag_up + diag_bot v, w = sl.eigsh(mat, 2, which = 'SM') On my pc the construction of the matrix mat takes 364ms, while only the last line in which the diagonalization is performed gives the reported error. Thanks a lot for the support. Simone

3 2

ANN: SfePy 2017.3
by Robert Cimrman 19 Sep '17

19 Sep '17

I am pleased to announce release 2017.3 of SfePy. Description ----------- SfePy (simple finite elements in Python) is a software for solving systems of coupled partial differential equations by the finite element method or by the isogeometric analysis (limited support). It is distributed under the new BSD license. Home page: http://sfepy.org Mailing list: https://mail.python.org/mm3/mailman3/lists/sfepy.python.org/ Git (source) repository, issue tracker: https://github.com/sfepy/sfepy Highlights of this release -------------------------- - support preconditioning in SciPy and PyAMG based linear solvers - user-defined preconditioners for PETSc linear solvers - parallel multiscale (macro-micro) homogenization-based computations - improved tutorial and installation instructions For full release notes see http://docs.sfepy.org/doc/release_notes.html#id1 (rather long and technical). Cheers, Robert Cimrman --- Contributors to this release in alphabetical order: Robert Cimrman Lubos Kejzlar Vladimir Lukes Matyas Novak

1 0