Mailman 3 November 2017 - SciPy-User

How to display a RGB image (MFISH)?
by jp April 2, 2023

April 2, 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

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 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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SciPy 1.0 released!
by Ralf Gommers Dec. 13, 2017

Dec. 13, 2017

Hi all, We are extremely pleased to announce the release of SciPy 1.0, 16 years after version 0.1 saw the light of day. It has been a long, productive journey to get here, and we anticipate many more exciting new features and releases in the future. Why 1.0 now? ------------ A version number should reflect the maturity of a project - and SciPy was a mature and stable library that is heavily used in production settings for a long time already. From that perspective, the 1.0 version number is long overdue. Some key project goals, both technical (e.g. Windows wheels and continuous integration) and organisational (a governance structure, code of conduct and a roadmap), have been achieved recently. Many of us are a bit perfectionist, and therefore are reluctant to call something "1.0" because it may imply that it's "finished" or "we are 100% happy with it". This is normal for many open source projects, however that doesn't make it right. We acknowledge to ourselves that it's not perfect, and there are some dusty corners left (that will probably always be the case). Despite that, SciPy is extremely useful to its users, on average has high quality code and documentation, and gives the stability and backwards compatibility guarantees that a 1.0 label imply. Some history and perspectives ----------------------------- - 2001: the first SciPy release - 2005: transition to NumPy - 2007: creation of scikits - 2008: scipy.spatial module and first Cython code added - 2010: moving to a 6-monthly release cycle - 2011: SciPy development moves to GitHub - 2011: Python 3 support - 2012: adding a sparse graph module and unified optimization interface - 2012: removal of scipy.maxentropy - 2013: continuous integration with TravisCI - 2015: adding Cython interface for BLAS/LAPACK and a benchmark suite - 2017: adding a unified C API with scipy.LowLevelCallable; removal of scipy.weave - 2017: SciPy 1.0 release **Pauli Virtanen** is SciPy's Benevolent Dictator For Life (BDFL). He says: *Truthfully speaking, we could have released a SciPy 1.0 a long time ago, so I'm happy we do it now at long last. The project has a long history, and during the years it has matured also as a software project. I believe it has well proved its merit to warrant a version number starting with unity.* *Since its conception 15+ years ago, SciPy has largely been written by and for scientists, to provide a box of basic tools that they need. Over time, the set of people active in its development has undergone some rotation, and we have evolved towards a somewhat more systematic approach to development. Regardless, this underlying drive has stayed the same, and I think it will also continue propelling the project forward in future. This is all good, since not long after 1.0 comes 1.1.* **Travis Oliphant** is one of SciPy's creators. He says: *I'm honored to write a note of congratulations to the SciPy developers and the entire SciPy community for the release of SciPy 1.0. This release represents a dream of many that has been patiently pursued by a stalwart group of pioneers for nearly 2 decades. Efforts have been broad and consistent over that time from many hundreds of people. From initial discussions to efforts coding and packaging to documentation efforts to extensive conference and community building, the SciPy effort has been a global phenomenon that it has been a privilege to participate in.* *The idea of SciPy was already in multiple people’s minds in 1997 when I first joined the Python community as a young graduate student who had just fallen in love with the expressibility and extensibility of Python. The internet was just starting to bringing together like-minded mathematicians and scientists in nascent electronically-connected communities. In 1998, there was a concerted discussion on the matrix-SIG, python mailing list with people like Paul Barrett, Joe Harrington, Perry Greenfield, Paul Dubois, Konrad Hinsen, David Ascher, and others. This discussion encouraged me in 1998 and 1999 to procrastinate my PhD and spend a lot of time writing extension modules to Python that mostly wrapped battle-tested Fortran and C-code making it available to the Python user. This work attracted the help of others like Robert Kern, Pearu Peterson and Eric Jones who joined their efforts with mine in 2000 so that by 2001, the first SciPy release was ready. This was long before Github simplified collaboration and input from others and the "patch" command and email was how you helped a project improve.* *Since that time, hundreds of people have spent an enormous amount of time improving the SciPy library and the community surrounding this library has dramatically grown. I stopped being able to participate actively in developing the SciPy library around 2010. Fortunately, at that time, Pauli Virtanen and Ralf Gommers picked up the pace of development supported by dozens of other key contributors such as David Cournapeau, Evgeni Burovski, Josef Perktold, and Warren Weckesser. While I have only been able to admire the development of SciPy from a distance for the past 7 years, I have never lost my love of the project and the concept of community-driven development. I remain driven even now by a desire to help sustain the development of not only the SciPy library but many other affiliated and related open-source projects. I am extremely pleased that SciPy is in the hands of a world-wide community of talented developers who will ensure that SciPy remains an example of how grass-roots, community-driven development can succeed.* **Fernando Perez** offers a wider community perspective: *The existence of a nascent Scipy library, and the incredible --if tiny by today's standards-- community surrounding it is what drew me into the scientific Python world while still a physics graduate student in 2001. Today, I am awed when I see these tools power everything from high school education to the research that led to the 2017 Nobel Prize in physics.* *Don't be fooled by the 1.0 number: this project is a mature cornerstone of the modern scientific computing ecosystem. I am grateful for the many who have made it possible, and hope to be able to contribute again to it in the future. My sincere congratulations to the whole team!* Highlights of this release -------------------------- 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. Upgrading and compatibility --------------------------- There have been a number of deprecations and API changes in this release, which are documented below. Before upgrading, we recommend that users check that their own code does not use deprecated SciPy functionality (to do so, run your code with ``python -Wd`` and check for ``DeprecationWarning`` s). 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. Authors ======= * @arcady + * @xoviat + * Anton Akhmerov * Dominic Antonacci + * Alessandro Pietro Bardelli * Ved Basu + * Michael James Bedford + * Ray Bell + * Juan M. Bello-Rivas + * Sebastian Berg * Felix Berkenkamp * Jyotirmoy Bhattacharya + * Matthew Brett * Jonathan Bright * Bruno Jiménez + * Evgeni Burovski * Patrick Callier * Mark Campanelli + * CJ Carey * Robert Cimrman * Adam Cox + * Michael Danilov + * David Haberthür + * Andras Deak + * Philip DeBoer * Anne-Sylvie Deutsch * Cathy Douglass + * Dominic Else + * Guo Fei + * Roman Feldbauer + * Yu Feng * Jaime Fernandez del Rio * Orestis Floros + * David Freese + * Adam Geitgey + * James Gerity + * Dezmond Goff + * Christoph Gohlke * Ralf Gommers * Dirk Gorissen + * Matt Haberland + * David Hagen + * Charles Harris * Lam Yuen Hei + * Jean Helie + * Gaute Hope + * Guillaume Horel + * Franziska Horn + * Yevhenii Hyzyla + * Vladislav Iakovlev + * Marvin Kastner + * Mher Kazandjian * Thomas Keck * Adam Kurkiewicz + * Ronan Lamy + * J.L. Lanfranchi + * Eric Larson * Denis Laxalde * Gregory R. Lee * Felix Lenders + * Evan Limanto * Julian Lukwata + * François Magimel * Syrtis Major + * Charles Masson + * Nikolay Mayorov * Tobias Megies * Markus Meister + * Roman Mirochnik + * Jordi Montes + * Nathan Musoke + * Andrew Nelson * M.J. Nichol * Juan Nunez-Iglesias * Arno Onken + * Nick Papior + * Dima Pasechnik + * Ashwin Pathak + * Oleksandr Pavlyk + * Stefan Peterson * Ilhan Polat * Andrey Portnoy + * Ravi Kumar Prasad + * Aman Pratik * Eric Quintero * Vedant Rathore + * Tyler Reddy * Joscha Reimer * Philipp Rentzsch + * Antonio Horta Ribeiro * Ned Richards + * Kevin Rose + * Benoit Rostykus + * Matt Ruffalo + * Eli Sadoff + * Pim Schellart * Nico Schlömer + * Klaus Sembritzki + * Nikolay Shebanov + * Jonathan Tammo Siebert * Scott Sievert * Max Silbiger + * Mandeep Singh + * Michael Stewart + * Jonathan Sutton + * Deep Tavker + * Martin Thoma * James Tocknell + * Aleksandar Trifunovic + * Paul van Mulbregt + * Jacob Vanderplas * Aditya Vijaykumar * Pauli Virtanen * James Webber * Warren Weckesser * Eric Wieser + * Josh Wilson * Zhiqing Xiao + * Evgeny Zhurko * Nikolay Zinov + * Zé Vinícius + A total of 121 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. Cheers, Ralf

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How to use scipy/fitpack Fortran subroutines from Fortran code
by Joe P Ninan Nov. 19, 2017

Nov. 19, 2017

Hi, I want to use some of the Fortran subroutines in scipy/fitpack directly from a Fortran code. (mainly the spline routines) Which I later plan to convert to a python callable module using f2py What is the best way to access these Fortran routines from another Fortran code? I want to avoid copy pasting the subroutines unnecessarily into my fortran code, if I can use the libraries from scipy installation on a computer. -cheers joe -- /--------------------------------------------------------------- "GNU/Linux: because a PC is a terrible thing to waste" - GNU Generation ************************************************ Joe Philip Ninan Postdoctoral Researcher 525 Davey Lab, Dept. of Astronomy & Astrophysics The Pennsylvania State University University Park, PA-16802 ------------------------------------------------------------ Website: https://indiajoe.gitlab.io/ My GnuPG Public Key: https://indiajoe.gitlab.io/files/JPN_public.key

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Announcing Theano 1.0.0
by Steven Bocco Nov. 16, 2017

Nov. 16, 2017

Announcing Theano 1.0.0 This is a release for a major version, with lots of new features, bug fixes, and some interface changes (deprecated or potentially misleading features were removed). Upgrading to Theano 1.0.0 is recommended for everyone, but you should first make sure that your code does not raise deprecation warnings with Theano 0.9*. Otherwise either results can change, or warnings may have been turned into errors. For those using the bleeding edge version in the git repository, we encourage you to update to the rel-1.0.0 tag. What's New Highlights (since 0.9.0): - Announcing that MILA will stop developing Theano <https://groups.google.com/d/msg/theano-users/7Poq8BZutbY/rNCIfvAEAwAJ> - conda packages now available and updated in our own conda channel mila-udem To install it: conda install -c mila-udem theano pygpu - Support NumPy 1.13 - Support pygpu 0.7 - Moved Python 3.* minimum supported version from 3.3 to 3.4 - Added conda recipe - Replaced deprecated package nose-parameterized with up-to-date package parameterized for Theano requirements - Theano now internally uses sha256 instead of md5 to work on systems that forbid md5 for security reason - Removed old GPU backend theano.sandbox.cuda. New backend theano.gpuarray is now the official GPU backend - Make sure MKL uses GNU OpenMP - *NB*: Matrix dot product (gemm) with mkl from conda could return wrong results in some cases. We have reported the problem upstream and we have a work around that raises an error with information about how to fix it. - Improved elemwise operations - Speed-up elemwise ops based on SciPy - Fixed memory leaks related to elemwise ops on GPU - Scan improvements - Speed up Theano scan compilation and gradient computation - Added meaningful message when missing inputs to scan - Speed up graph toposort algorithm - Faster C compilation by massively using a new interface for op params - Faster optimization step, with new optional destroy handler - Documentation updated and more complete - Added documentation for RNNBlock - Updated conv documentation - Support more debuggers for PdbBreakpoint - Many bug fixes, crash fixes and warning improvements Interface changes: - Merged duplicated diagonal functions into two ops: ExtractDiag (extract a diagonal to a vector), and AllocDiag (set a vector as a diagonal of an empty array) - Removed op ExtractDiag from theano.tensor.nlinalg, now only in theano.tensor.basic - Generalized AllocDiag for any non-scalar input - Added new parameter target for MRG functions - Renamed MultinomialWOReplacementFromUniform to ChoiceFromUniform - Changed grad() method to L_op() in ops that need the outputs to compute gradient - Removed or deprecated Theano flags: - cublas.lib - cuda.enabled - enable_initial_driver_test - gpuarray.sync - home - lib.cnmem - nvcc.* flags - pycuda.init Convolution updates: - Implemented separable convolutions for 2D and 3D - Implemented grouped convolutions for 2D and 3D - Added dilated causal convolutions for 2D - Added unshared convolutions - Implemented fractional bilinear upsampling - Removed old conv3d interface - Deprecated old conv2d interface GPU: - Added a meta-optimizer to select the fastest GPU implementations for convolutions - Prevent GPU initialization when not required - Added disk caching option for kernels - Added method my_theano_function.sync_shared() to help synchronize GPU Theano functions - Added useful stats for GPU in profile mode - Added Cholesky op based on cusolver backend - Added GPU ops based on magma library <http://icl.cs.utk.edu/magma/software/>: SVD, matrix inverse, QR, cholesky and eigh - Added GpuCublasTriangularSolve - Added atomic addition and exchange for long long values in GpuAdvancedIncSubtensor1_dev20 - Support log gamma function for all non-complex types - Support GPU SoftMax in both OpenCL and CUDA - Support offset parameter k for GpuEye - CrossentropyCategorical1Hot and its gradient are now lifted to GPU - cuDNN: - Official support for v6.* and v7.* - Added spatial transformation operation based on cuDNN - Updated and improved caching system for runtime-chosen cuDNN convolution algorithms - Support cuDNN v7 tensor core operations for convolutions with runtime timed algorithms - Better support and loading on Windows and Mac - Support cuDNN v6 dilated convolutions - Support cuDNN v6 reductions for contiguous inputs - Optimized SUM(x^2), SUM(ABS(X)) and MAX(ABS(X)) operations with cuDNN reductions - Added new Theano flags cuda.include_path, dnn.base_path and dnn.bin_path to help configure Theano when CUDA and cuDNN can not be found automatically - Extended Theano flag dnn.enabled with new option no_check to help speed up cuDNN importation - Disallowed float16 precision for convolution gradients - Fixed memory alignment detection - Added profiling in C debug mode (with theano flag cmodule.debug=True ) - Added Python scripts to help test cuDNN convolutions - Automatic addition of cuDNN DLL path to PATH environment variable on Windows - Updated float16 support - Added documentation for GPU float16 ops - Support float16 for GpuGemmBatch - Started to use float32 precision for computations that don't support float16 on GPU New features: - Implemented truncated normal distribution with box-muller transform - Added L_op() overriding option for OpFromGraph - Added NumPy C-API based fallback implementation for [sd]gemv_ and [sd]dot_ - Implemented topk and argtopk on CPU and GPU - Implemented max() and min() functions for booleans and unsigned integers types - Added tensor6() and tensor7() in theano.tensor module - Added boolean indexing for sub-tensors - Added covariance matrix function theano.tensor.cov - Added a wrapper for Baidu's CTC <https://github.com/baidu-research/warp-ctc> cost and gradient functions - Added scalar and elemwise CPU ops for modified Bessel function of order 0 and 1 from scipy.special - Added Scaled Exponential Linear Unit (SELU) activation - Added sigmoid_binary_crossentropy function - Added tri-gamma function - Added unravel_index and ravel_multi_index functions on CPU - Added modes half and full for Images2Neibs ops - Implemented gradient for AbstractBatchNormTrainGrad - Implemented gradient for matrix pseudoinverse op - Added new prop replace for ChoiceFromUniform op - Added new prop on_error for CPU Cholesky op - Added new Theano flag deterministic to help control how Theano optimize certain ops that have deterministic versions. Currently used for subtensor Ops only. - Added new Theano flag cycle_detection to speed-up optimization step by reducing time spending in inplace optimizations - Added new Theano flag check_stack_trace to help check the stack trace during optimization process - Added new Theano flag cmodule.debug to allow a debug mode for Theano C code. Currently used for cuDNN convolutions only. - Added new Theano flag pickle_test_value to help disable pickling test values Others: - Kept stack trace for optimizations in new GPU backend - Added deprecation warning for the softmax and logsoftmax vector case - Added a warning to announce that C++ compiler will become mandatory in next Theano release 0.11 - Added R_op() for ZeroGrad - Added description for rnnblock Other more detailed changes: - Fixed invalid casts and index overflows in theano.tensor.signal.pool - Fixed gradient error for elemwise minimum and maximum when compared values are the same - Fixed gradient for ARange - Removed ViewOp subclass during optimization - Removed useless warning when profile is manually disabled - Added tests for abstract conv - Added options for disconnected_outputs to Rop - Removed theano/compat/six.py - Removed COp.get_op_params() - Support of list of strings for Op.c_support_code(), to help not duplicate support codes - Macro names provided for array properties are now standardized in both CPU and GPU C codes - Moved all C code files into separate folder c_code in every Theano module - Many improvements for Travis CI tests (with better splitting for faster testing) - Many improvements for Jenkins CI tests: daily testings on Mac and Windows in addition to Linux Download and Install You can download Theano from http://pypi.python.org/pypi/Theano Installation instructions are available at http://deeplearning.net/software/theano/install.html Description Theano is a Python library that allows you to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays. It is built on top of NumPy. Theano features: - tight integration with NumPy: a similar interface to NumPy's. numpy.ndarrays are also used internally in Theano-compiled functions. - transparent use of a GPU: perform data-intensive computations much faster than on a CPU. - efficient symbolic differentiation: Theano can compute derivatives for functions of one or many inputs. - speed and stability optimizations: avoid nasty bugs when computing expressions such as log(1+ exp(x)) for large values of x. - dynamic C code generation: evaluate expressions faster. - extensive unit-testing and self-verification: includes tools for detecting and diagnosing bugs and/or potential problems. Theano has been powering large-scale computationally intensive scientific research since 2007, but it is also approachable enough to be used in the classroom (IFT6266 at the University of Montreal). Resources About Theano: http://deeplearning.net/software/theano/ Theano-related projects: http://github.com/Theano/Theano/wiki/Related-projects About NumPy: http://numpy.scipy.org/ About SciPy: http://www.scipy.org/ Machine Learning Tutorial with Theano on Deep Architectures: http://deeplearning.net/tutorial/ Acknowledgments I would like to thank all contributors of Theano. Since release 0.9.0, many people have helped, notably (in alphabetical order): - Aarni Koskela - Adam Becker - Adam Geitgey - Adrian Keet - Adrian Seyboldt - Aleksandar Botev - Alexander Matyasko - amrithasuresh - Andrei Costinescu - Anirudh Goyal - Anmol Sahoo - Arnaud Bergeron - Bogdan Budescu - Boris Fomitchev - Cesar Laurent - Chiheb Trabelsi - Chong Wu - dareneiri - Daren Eiri - Dzmitry Bahdanau - erakra - Faruk Ahmed - Florian Bordes - fo40225 - Frederic Bastien - Gabe Schwartz - Ghislain Antony Vaillant - Gijs van Tulder - Holger Kohr - Jan Schlüter - Jayanth Koushik - Jeff Donahue - jhelie - João Victor Tozatti Risso - Joseph Paul Cohen - Juan Camilo Gamboa Higuera - Laurent Dinh - Lilian Besson - lrast - Lv Tao - Matt Graham - Michael Manukyan - Mohamed Ishmael Diwan Belghazi - Mohammed Affan - morrme - mrTsjolder - Murugesh Marvel - naitonium - NALEPA - Nan Jiang - Pascal Lamblin - Ramana Subramanyam - Rebecca N. Palmer - Reyhane Askari - Saizheng Zhang - Shawn Tan - Shubh Vachher - Simon Lefrancois - Sina Honari - Steven Bocco - Tegan Maharaj - Thomas George - Tim Cooijmans - Vikram - vipulraheja - wyjw - Xavier Bouthillier - xiaoqie - Yikang Shen - Zhouhan LIN - Zotov Yuriy Also, thank you to all NumPy and Scipy developers as Theano builds on their strengths. All questions/comments are always welcome on the Theano mailing-lists ( http://deeplearning.net/software/theano/#community ) -- Steven Bocco

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Python @ FOSDEM 2018
by Pierre de Buyl Nov. 2, 2017

Nov. 2, 2017

Dear SciPythonists and NumPythonists, FOSDEM is a free event for software developers to meet, share ideas and collaborate. Every year, 6500+ of developers of free and open source software from all over the world gather at the event in Brussels. Every year, 6500+ of developers of free and open source software from all over the world gather at the event in Brussels. For FOSDEM 2018, we will try the new concept of a virtual Python-devroom: there is no dedicated Python room but instead, we promote the presence of Python in all devrooms. We hope to have at least one Python talk in every devroom (Yes, even in Perl, Ada, Go and Rust devrooms ;-) ). How can you help to highlight the Python community at Python-FOSDEM 2018? Propose your talk in the closest related devroom: https://fosdem.org/2018/news/2017-10-04-accepted-developer-rooms/ Not all devrooms are language-specific and a number of topics come to mind for data and science participants: "Monitoring & Cloud devroom" https://lists.fosdem.org/pipermail/fosdem/2017-October/002631.html "HPC, Big Data, and Data Science" https://lists.fosdem.org/pipermail/fosdem/2017-October/002615.html "LLVM toolchain" https://lists.fosdem.org/pipermail/fosdem/2017-October/002624.html Most call for contributions end around the 24 of november. Send a copy of your proposition to python-devroom AT lists.fosdem DOT org. We will publish a dedicated schedule for Python on https://python-fosdem.org/ and at our stand. A dinner will be also organized, stay tuned. We are waiting for your talks proposals. The Python-FOSDEM committee

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