Mailman 3 March 2017 - NumPy-Discussion

Advanced numpy tutorial for SciPy?
by Chris Barker - NOAA Federal 22 Mar '17

22 Mar '17

In another thread, there is a discussion of a workshop on "Taking NumPy In Stride" for PyData Barcelona. I think it would be great to have something like that at SciPy in Austin this year. Jaime can't make it, and I don't think strides are going to fill a four hour tutorial, so it would be good as part of an advanced numpy tutorial. I don't have the bandwidth to put together an entire tutorial, but maybe someone would like to join forces? Or if someone is already planning an advanced numpy tutorial, perhaps I could contribute. Not much time left to get a proposal in! -Chris

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Announcing Theano 0.9.0
by Steven Bocco 21 Mar '17

21 Mar '17

Announcing Theano 0.9.0 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). This release is the last major version that features the old GPU back-end ( theano.sandbox.cuda, accessible through device=gpu*). All GPU users are encouraged to transition to the new GPU back-end, based on libgpuarray ( theano.gpuarray, accessible through device=cuda*). For more information, see https://github.com/Theano/Theano/wiki/Converting-to-the-new-gpu-back-end%28… . Upgrading to Theano 0.9.0 is recommended for everyone, but you should first make sure that your code does not raise deprecation warnings with Theano 0.8*. 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-0.9.0 tag. What's New Highlights (since 0.8.0): - Better Python 3.5 support - Better numpy 1.12 support - Conda packages for Mac, Linux and Windows - Support newer Mac and Windows versions - More Windows integration: - Theano scripts (theano-cache and theano-nose) now works on Windows - Better support for Windows end-lines into C codes - Support for space in paths on Windows - Scan improvements: - More scan optimizations, with faster compilation and gradient computation - Support for checkpoint in scan (trade off between speed and memory usage, useful for long sequences) - Fixed broadcast checking in scan - Graphs improvements: - More numerical stability by default for some graphs - Better handling of corner cases for theano functions and graph optimizations - More graph optimizations with faster compilation and execution - smaller and more readable graph - New GPU back-end: - Removed warp-synchronous programming to get good results with newer CUDA drivers - More pooling support on GPU when cuDNN isn't available - Full support of ignore_border option for pooling - Inplace storage for shared variables - float16 storage - Using PCI bus ID of graphic cards for a better mapping between theano device number and nvidia-smi number - Fixed offset error in GpuIncSubtensor - Less C code compilation - Added support for bool dtype - Updated and more complete documentation - Bug fixes related to merge optimizer and shape inference - Lot of other bug fixes, crashes fixes and warning improvements Interface changes: - Merged CumsumOp/CumprodOp into CumOp - In MRG module: - Replaced method multinomial_wo_replacement() with new method choice() - Random generator now tries to infer the broadcast pattern of its output - New pooling interface - Pooling parameters can change at run time - Moved softsign out of sandbox to theano.tensor.nnet.softsign - Using floatX dtype when converting empty list/tuple - Roll make the shift be modulo the size of the axis we roll on - round() default to the same as NumPy: half_to_even Convolution updates: - Support of full and half modes for 2D and 3D convolutions including in conv3d2d - Allowed pooling of empty batch - Implement conv2d_transpose convenience function - Multi-cores convolution and pooling on CPU - New abstract 3d convolution interface similar to the 2d convolution interface - Dilated convolution GPU: - cuDNN: support versoin 5.1 and wrap batch normalization (2d and 3d) and RNN functions - Multiple-GPU, synchrone update (via platoon, use NCCL) - Gemv(matrix-vector product) speed up for special shape - cublas gemv workaround when we reduce on an axis with a dimensions size of 0 - Warn user that some cuDNN algorithms may produce unexpected results in certain environments for convolution backward filter operations - GPUMultinomialFromUniform op now supports multiple dtypes - Support for MaxAndArgMax for some axis combination - Support for solve (using cusolver), erfinv and erfcinv - Implemented GpuAdvancedSubtensor New features: - OpFromGraph now allows gradient overriding for every input - Added Abstract Ops for batch normalization that use cuDNN when available and pure Theano CPU/GPU alternatives otherwise - Added gradient of solve, tensorinv (CPU), tensorsolve (CPU), searchsorted (CPU), DownsampleFactorMaxGradGrad (CPU) - Added Multinomial Without Replacement - Allowed partial evaluation of compiled function - More Rop support - Indexing support ellipsis: a[..., 3]`, a[1,...,3] - Added theano.tensor.{tensor5,dtensor5, ...} - compiledir_format support device - Added New Theano flag conv.assert_shape to check user-provided shapes at runtime (for debugging) - Added new Theano flag cmodule.age_thresh_use - Added new Theano flag cuda.enabled - Added new Theano flag nvcc.cudafe to enable faster compilation and import with old CUDA back-end - Added new Theano flag print_global_stats to print some global statistics (time spent) at the end - Added new Theano flag profiling.ignore_first_call, useful to profile the new gpu back-end - remove ProfileMode (use Theano flag profile=True instead) Others: - Split op now has C code for CPU and GPU - theano-cache list now includes compilation times - Speed up argmax only on GPU (without also needing the max) - More stack trace in error messages - Speed up cholesky grad - log(sum(exp(...))) now get stability optimized Other more detailed changes: - Added Jenkins (gpu tests run on pull requests in addition to daily buildbot) - Removed old benchmark directory and other old files not used anymore - Use of 64-bit indexing in sparse ops to allow matrix with more then 231-1 elements - Allowed more then one output to be an destructive inplace - More support of negative axis - Added the keepdims parameter to the norm function - Make scan gradient more deterministic 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. For this particular release, many people have helped, notably (in alphabetical order): - affanv14 - Alexander Matyasko - Alexandre de Brebisson - Amjad Almahairi - Andrés Gottlieb - Anton Chechetka - Arnaud Bergeron - Benjamin Scellier - Ben Poole - Bhavishya Pohani - Bryn Keller - Caglar - Carl Thomé - Cesar Laurent - Chiheb Trabelsi - Chinnadhurai Sankar - Christos Tsirigotis - Ciyong Chen - David Bau - Dimitar Dimitrov - Evelyn Mitchell - Fábio Perez - Faruk Ahmed - Fei Wang - Fei Zhan - Florian Bordes - Francesco Visin - Frederic Bastien - Fuchai - Gennadiy Tupitsin - Gijs van Tulder - Gilles Louppe - Gokula Krishnan - Greg Ciccarelli - gw0 [http://gw.tnode.com/] - happygds - Harm de Vries - He - hexahedria - hsintone - Huan Zhang - Ilya Kulikov - Iulian Vlad Serban - jakirkham - Jakub Sygnowski - Jan Schlüter - Jesse Livezey - Jonas Degrave - joncrall - Kaixhin - Karthik Karanth - Kelvin Xu - Kevin Keraudren - khaotik - Kirill Bobyrev - Kumar Krishna Agrawal - Kv Manohar - Liwei Cai - Lucas Beyer - Maltimore - Marc-Alexandre Cote - Marco - Marius F. Killinger - Martin Drawitsch - Mathieu Germain - Matt Graham - Maxim Kochurov - Micah Bojrab - Michael Harradon - Mikhail Korobov - mockingjamie - Mohammad Pezeshki - Morgan Stuart - Nan Rosemary Ke - Neil - Nicolas Ballas - Nizar Assaf - Olivier Mastropietro - Ozan Çağlayan - p - Pascal Lamblin - Pierre Luc Carrier - RadhikaG - Ramana Subramanyam - Ray Donnelly - Rebecca N. Palmer - Reyhane Askari - Rithesh Kumar - Rizky Luthfianto - Robin Millette - Roman Ring - root - Ruslana Makovetsky - Saizheng Zhang - Samira Ebrahimi Kahou - Samira Shabanian - Sander Dieleman - Sebastin Santy - Shawn Tan - Simon Lefrancois - Sina Honari - Steven Bocco - superantichrist - Taesup (TS) Kim - texot - Thomas George - tillahoffmann - Tim Cooijmans - Tim Gasper - valtron - Vincent Dumoulin - Vincent Michalski - Vitaliy Kurlin - Wazeer Zulfikar - wazeerzulfikar - Wojciech Głogowski - Xavier Bouthillier - Yang Zhang - Yann N. Dauphin - Yaroslav Ganin - Ying Zhang - you-n-g - Zhouhan LIN 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 )

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NumPy 1.12.1 released
by Charles R Harris 18 Mar '17

18 Mar '17

Hi All, I'm pleased to announce the release of NumPy 1.12.1. NumPy 1.12.1 supports Python 2.7 and 3.4 - 3.6 and fixes bugs and regressions found in NumPy 1.12.0. In particular, the regression in f2py constant parsing is fixed. Wheels for Linux, Windows, and OSX can be found on pypi. Archives can be downloaded from github <https://github.com/numpy/numpy/releases/tag/v1.12.1>. *Contributors* A total of 10 people contributed to this release. People with a "+" by their names contributed a patch for the first time. * Charles Harris * Eric Wieser * Greg Young * Joerg Behrmann + * John Kirkham * Julian Taylor * Marten van Kerkwijk * Matthew Brett * Shota Kawabuchi * Jean Utke + *Fixes Backported* * #8483: BUG: Fix wrong future nat warning and equiv type logic error... * #8489: BUG: Fix wrong masked median for some special cases * #8490: DOC: Place np.average in inline code * #8491: TST: Work around isfinite inconsistency on i386 * #8494: BUG: Guard against replacing constants without `'_'` spec in f2py. * #8524: BUG: Fix mean for float 16 non-array inputs for 1.12 * #8571: BUG: Fix calling python api with error set and minor leaks for... * #8602: BUG: Make iscomplexobj compatible with custom dtypes again * #8618: BUG: Fix undefined behaviour induced by bad `__array_wrap__` * #8648: BUG: Fix `MaskedArray.__setitem__` * #8659: BUG: PPC64el machines are POWER for Fortran in f2py * #8665: BUG: Look up methods on MaskedArray in `_frommethod` * #8674: BUG: Remove extra digit in `binary_repr` at limit * #8704: BUG: Fix deepcopy regression for empty arrays. * #8707: BUG: Fix ma.median for empty ndarrays Cheers, Chuck

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ANN: xtensor 0.7.1 numpy-style syntax in C++ with bindings to numpy arrays
by Sylvain Corlay 17 Mar '17

17 Mar '17

Hi All, On behalf of the xtensor development team, I am pleased to announce the releases of - xtensor 0.7.1 https://github.com/QuantStack/xtensor/ - xtensor-python 0.6.0 https://github.com/QuantStack/xtensor-python/ *What is xtensor?* xtensor is a C++ library meant for numerical analysis with multi-dimensional array expressions. xtensor provides - an extensible expression system enabling* lazy broadcasting.* - an API following the idioms of the *C++ standard library*. - an increasing support of numpy features which you can see in the *NumPy to xtensor cheat sheet*. - tools to manipulate array expressions and build upon xtensor. - *numpy bindings* enabling the inplace use of numpy arrays as xtensor expressions in C++ extensions. *What is new in this release?* In this release, we have added the reducers functionality and the real and imaginary views for complex arrays. We have increased the performance of xtensor universal functions. *Where can I learn more about xtensor?* Check out the extensive documentation: http://xtensor.readthedocs.io/en/latest/ Or the numpy to xtensor cheat sheet: http://xtensor.readthedocs.io/en/latest/numpy.html Or join us in the chat room: https://gitter.im/QuantStack/Lobby Thanks! Sylvain >From NumPy to xtensor Containers Two container types are provided. xarray (dynamic number of dimensions) and xtensor (static number of dimensions). Python 3 - numpyC++ 14 - xtensor np.array([[3, 4], [5, 6]]) xt::xarray<double>({{3, 4}, {5, 6}}) xt::xtensor<double, 2>({{3, 4}, {5, 6}}) arr.reshape([3, 4]) arr.reshape{{3, 4}) Initializers Lazy helper functions return tensor expressions. Return types don’t hold any value and are evaluated upon access or assignment. They can be assigned to a container or directly used in expressions. Python 3 - numpyC++ 14 - xtensor np.linspace(1.0, 10.0, 100) xt::linspace<double>(1.0, 10.0, 100) np.logspace(2.0, 3.0, 4) xt::logspace<double>(2.0, 3.0, 4) np.arange(3, 7) xt::arange(3, 7) np.eye(4) xt::eye(4) np.zeros([3, 4]) xt::zeros<double>({3, 4}) np.ones([3, 4]) xt::ones<double>({3, 4}) np.meshgrid(x0, x1, x2, indexing='ij') xt::meshgrid(x0, x1, x2) xtensor’s meshgrid implementation corresponds to numpy’s 'ij' indexing order. Broadcasting xtensor offers lazy numpy-style broadcasting, and universal functions. Unlike numpy, no copy or temporary variables are created. Python 3 - numpyC++ 14 - xtensor a[:, np.newaxis] a[:5, 1:] a[5:1:-1, :] xt::view(a, xt::all(), xt::newaxis()) xt::view(a, xt::range(_, 5), xt::range(1, _)) xt::view(a, xt::range(5, 1, -1), xt::all()) np.broadcast(a, [4, 5, 7]) xt::broadcast(a, {4, 5, 7}) np.vectorize(f) xt::vectorize(f) a[a > 5] xt::filter(a, a > 5) a[[0, 1], [0, 0]] xt::index_view(a, {{0, 0}, {1, 0}}) Random The random module provides simple ways to create random tensor expressions, lazily. Python 3 - numpyC++ 14 - xtensor np.random.randn(10, 10) xt::random::randn<double>({10, 10}) np.random.randint(10, 10) xt::random::randint<int>({10, 10}) np.random.rand(3, 4) xt::random::rand<double>({3, 4}) Concatenation Concatenating expressions does not allocate memory, it returns a tensor expression holding closures on the specified arguments. Python 3 - numpyC++ 14 - xtensor np.stack([a, b, c], axis=1) xt::stack(xtuple(a, b, c), 1) np.concatenate([a, b, c], axis=1) xt::concatenate(xtuple(a, b, c), 1) Diagonal, triangular and flip In the same spirit as concatenation, the following operations do not allocate any memory and do not modify the underlying xexpression. Python 3 - numpyC++ 14 - xtensor np.diag(a) xt::diag(a) np.diagonal(a) xt::diagonal(a) np.triu(a) xt::triu(a) np.tril(a, k=1) xt::tril(a, 1) np.flip(a, axis=3) xt::flip(a, 3) np.flipud(a) xt::flip(a, 0) np.fliplr(a) xt::flip(a, 1) Iteration xtensor follows the idioms of the C++ STL providing iterator pairs to iterate on arrays in different fashions. Python 3 - numpyC++ 14 - xtensor for x in np.nditer(a): for(auto it=a.xbegin(); it!=a.xend(); ++it) Iterating with a prescribed broadcasting shape for(auto it=a.xbegin({3, 4}); it!=a.xend({3, 4}); ++it) Logical Logical universal functions are truly lazy. xt::where(condition, a, b) does not evaluate a where condition is falsy, and it does not evaluate b where condition is truthy. Python 3 - numpyC++ 14 - xtensor np.where(a > 5, a, b) xt::where(a > 5, a, b) np.where(a > 5) xt::where(a > 5) np.any(a) xt::any(a) np.all(a) xt::all(a) np.logical_and(a, b) a && b np.logical_or(a, b) a || b Comparisons Python 3 - numpyC++ 14 - xtensor np.equal(a, b) xt::equal(a, b) np.not_equal(a) xt::not_equal(a) np.nonzero(a) xt::nonzero(a) Complex numbers Functions xt::real and xt::imag respectively return views on the real and imaginary part of a complex expression. The returned value is an expression holding a closure on the passed argument. Python 3 - numpyC++ 14 - xtensor np.real(a) xt::real(a) np.imag(a) xt::imag(a) - The constness and value category (rvalue / lvalue) of real(a) is the same as that of a. Hence, if a is a non-const lvalue, real(a) is an non-const lvalue reference, to which one can assign a real expression. - If a has complex values, the same holds for imag(a). The constness and value category ofimag(a) is the same as that of a. - If a has real values, imag(a) returns zeros(a.shape()). Reducers Reducers accumulate values of tensor expressions along specified axes. When no axis is specified, values are accumulated along all axes. Reducers are lazy, meaning that returned expressons don’t hold any values and are computed upon access or assigmnent. Python 3 - numpyC++ 14 - xtensor np.sum(a, axis=[0, 1]) xt::sum(a, {0, 1}) np.sum(a) xt::sum(a) np.prod(a, axis=1) xt::prod(a, {1}) np.prod(a) xt::prod(a) np.mean(a, axis=1) xt::mean(a, {1}) np.mean(a) xt::mean(a) More generally, one can use the xt::reduce(function, input, axes) which allows the specification of an arbitrary binary function for the reduction. The binary function must be cummutative and associative up to rounding errors. Mathematical functions xtensor universal functions are provided for a large set number of mathematical functions. *Basic functions:* Python 3 - numpyC++ 14 - xtensor np.isnan(a) xt::isnan(a) np.absolute(a) xt::abs(a) np.sign(a) xt::sign(a) np.remainder(a, b) xt::remainder(a, b) np.clip(a, min, max) xt::clip(a, min, max) xt::fma(a, b, c) *Exponential functions:* Python 3 - numpyC++ 14 - xtensor np.exp(a) xt::exp(a) np.expm1(a) xt::expm1(a) np.log(a) xt::log(a) np.log1p(a) xt::log1p(a) *Power functions:* Python 3 - numpyC++ 14 - xtensor np.power(a, p) xt::pow(a, b) np.sqrt(a) xt::sqrt(a) np.cbrt(a) xt::cbrt(a) *Trigonometric functions:* Python 3 - numpyC++ 14 - xtensor np.sin(a) xt::sin(a) np.cos(a) xt::cos(a) np.tan(a) xt::tan(a) *Hyperbolic functions:* Python 3 - numpyC++ 14 - xtensor np.sinh(a) xt::sinh(a) np.cosh(a) xt::cosh(a) np.tang(a) xt::tanh(a) *Error and gamma functions:* Python 3 - numpyC++ 14 - xtensor scipy.special.erf(a) xt::erf(a) scipy.special.gamma(a) xt::tgamma(a) scipy.special.gammaln(a) xt::lgamma(a)

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Move scipy.org docs to Github?
by Matthew Brett 17 Mar '17

17 Mar '17

Hi, The scipy.org site is down at the moment, and has been for more than 36 hours: https://github.com/numpy/numpy/issues/8779#issue-213781439 This has happened before: https://github.com/scipy/scipy.org/issues/187#issue-186426408 I think it was down for about 24 hours that time. >From the number of people opening issues or commenting on the scipy.org website this time, it seems to be causing quite a bit of disruption. It seems to me that we would have a much better chances of avoiding significant down-time, if we switched to hosting the docs on github pages. What do y'all think? Cheers, Matthew

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caching large allocations on gnu/linux
by Julian Taylor 14 Mar '17

14 Mar '17

Hi, As numpy often allocates large arrays and one factor in its performance is faulting memory from the kernel to the process. This has some cost that is relatively significant. For example in this operation on large arrays it accounts for 10-15% of the runtime: import numpy as np a = np.ones(10000000) b = np.ones(10000000) %timeit (a * b)**2 + 3 54.45% ipython umath.so [.] sse2_binary_multiply_DOUBLE 20.43% ipython umath.so [.] DOUBLE_add 16.66% ipython [kernel.kallsyms] [k] clear_page The reason for this is that the glibc memory allocator uses memory mapping for large allocations instead of reusing already faulted memory. The reason for this is to return memory back to the system immediately when it is free to keep the whole system more robust. This makes a lot of sense in general but not so much for many numerical applications that often are the only thing running. But despite if have been shown in an old paper that caching memory in numpy speeds up many applications, numpys usage is diverse so we couldn't really diverge from the glibc behaviour. Until Linux 4.5 added support for madvise(MADV_FREE). This flag of the madvise syscall tells the kernel that a piece of memory can be reused by other processes if there is memory pressure. Should another process claim the memory and the original process want to use it again the kernel will fault new memory into its place so it behaves exactly as if it was just freed regularly. But when no other process claims the memory and the original process wants to reuse it, the memory do not need to be faulted again. So effectively this flag allows us to cache memory inside numpy that can be reused by the rest of the system if required. Doing gives the expected speedup in the above example. An issue is that the memory usage of numpy applications will seem to increase. The memory that is actually free will still show up in the usual places you look at memory usage. Namely the resident memory usage of the process in top, /proc etc. The usage will only go down when the memory is actually needed by other processes. This probably would break some of the memory profiling tools so probably we need a switch to disable the caching for the profiling tools to use. Another concern is that using this functionality is actually the job of the system memory allocator but I had a look at glibcs allocator and it does not look like an easy job to make good use of MADV_FREE retroactively, so I don't expect this to happen anytime soon. Should it be agreed that caching is worthwhile I would propose a very simple implementation. We only really need to cache a small handful of array data pointers for the fast allocate deallocate cycle that appear in common numpy usage. For example a small list of maybe 4 pointers storing the 4 largest recent deallocations. New allocations just pick the first memory block of sufficient size. The cache would only be active on systems that support MADV_FREE (which is linux 4.5 and probably BSD too). So what do you think of this idea? cheers, Julian

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docs.scipy.org down
by Ryan May 13 Mar '17

13 Mar '17

Is https://docs.scipy.org/ being down known issue? Ryan -- Ryan May

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float16/32: wrong number of digits?
by Nico Schlömer 13 Mar '17

13 Mar '17

Hi everyone, I wondered how to express a numpy float exactly in terms of format, and found `%r` quite useful: `float(repr(a)) == a` is guaranteed for Python `float`s. When trying the same thing with lower-precision Python floats, I found this identity not quite fulfilled: ``` import numpy b = numpy.array([1.0 / 3.0], dtype=np.float16) float(repr(b[0])) - b[0] Out[12]: -1.9531250000093259e-06 ``` Indeed, ``` b Out[6]: array([ 0.33325195], dtype=float16) ``` ``` repr(b[0]) Out[7]: '0.33325' ``` When counting the bits, a float16 should hold 4.8 decimal digits, so `repr()` seems right. Where does the garbage tail -1.9531250000093259e-06 come from though? Cheers, Nico

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Building with Clang on Windows?
by Matthew Brett 10 Mar '17

10 Mar '17

Hi, Has anyone succeeded in building numpy with Clang on Windows? Or, building any other Python extension with Clang on Windows? I'd love to hear about how you did it... Cheers, Matthew

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ANN: SciPy 0.19.0
by Evgeni Burovski 09 Mar '17

09 Mar '17

On behalf of the Scipy development team I am pleased to announce the availability of Scipy 0.19.0. This release contains several great new features and a large number of bug fixes and various improvements, as detailed in the release notes below. 121 people contributed to this release over the course of seven months. Thanks to everyone who contributed! This release requires Python 2.7 or 3.4-3.6 and NumPy 1.8.2 or greater. Source tarballs and release notes can be found at https://github.com/scipy/scipy/releases/tag/v0.19.0. OS X and Linux wheels are available from PyPI. For security-conscious, the wheels themselves are signed with my GPG key. Additionally, you can checksum the wheels and verify the checksums with those listed below or in the README file at https://github.com/scipy/scipy/releases/tag/v0.19.0. Cheers, Evgeni -----BEGIN PGP SIGNED MESSAGE----- Hash: SHA256 ========================== SciPy 0.19.0 Release Notes ========================== .. contents:: SciPy 0.19.0 is the culmination of 7 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 0.19.x branch, and on adding new features on the master branch. This release requires Python 2.7 or 3.4-3.6 and NumPy 1.8.2 or greater. Highlights of this release include: - - A unified foreign function interface layer, `scipy.LowLevelCallable`. - - Cython API for scalar, typed versions of the universal functions from the `scipy.special` module, via `cimport scipy.special.cython_special`. New features ============ Foreign function interface improvements - --------------------------------------- `scipy.LowLevelCallable` provides a new unified interface for wrapping low-level compiled callback functions in the Python space. It supports Cython imported "api" functions, ctypes function pointers, CFFI function pointers, ``PyCapsules``, Numba jitted functions and more. See `gh-6509 <https://github.com/scipy/scipy/pull/6509>`_ for details. `scipy.linalg` improvements - --------------------------- The function `scipy.linalg.solve` obtained two more keywords ``assume_a`` and ``transposed``. The underlying LAPACK routines are replaced with "expert" versions and now can also be used to solve symmetric, hermitian and positive definite coefficient matrices. Moreover, ill-conditioned matrices now cause a warning to be emitted with the estimated condition number information. Old ``sym_pos`` keyword is kept for backwards compatibility reasons however it is identical to using ``assume_a='pos'``. Moreover, the ``debug`` keyword, which had no function but only printing the ``overwrite_<a, b>`` values, is deprecated. The function `scipy.linalg.matrix_balance` was added to perform the so-called matrix balancing using the LAPACK xGEBAL routine family. This can be used to approximately equate the row and column norms through diagonal similarity transformations. The functions `scipy.linalg.solve_continuous_are` and `scipy.linalg.solve_discrete_are` have numerically more stable algorithms. These functions can also solve generalized algebraic matrix Riccati equations. Moreover, both gained a ``balanced`` keyword to turn balancing on and off. `scipy.spatial` improvements - ---------------------------- `scipy.spatial.SphericalVoronoi.sort_vertices_of_regions` has been re-written in Cython to improve performance. `scipy.spatial.SphericalVoronoi` can handle > 200 k points (at least 10 million) and has improved performance. The function `scipy.spatial.distance.directed_hausdorff` was added to calculate the directed Hausdorff distance. ``count_neighbors`` method of `scipy.spatial.cKDTree` gained an ability to perform weighted pair counting via the new keywords ``weights`` and ``cumulative``. See `gh-5647 <https://github.com/scipy/scipy/pull/5647>`_ for details. `scipy.spatial.distance.pdist` and `scipy.spatial.distance.cdist` now support non-double custom metrics. `scipy.ndimage` improvements - ---------------------------- The callback function C API supports PyCapsules in Python 2.7 Multidimensional filters now allow having different extrapolation modes for different axes. `scipy.optimize` improvements - ----------------------------- The `scipy.optimize.basinhopping` global minimizer obtained a new keyword, `seed`, which can be used to seed the random number generator and obtain repeatable minimizations. The keyword `sigma` in `scipy.optimize.curve_fit` was overloaded to also accept the covariance matrix of errors in the data. `scipy.signal` improvements - --------------------------- The function `scipy.signal.correlate` and `scipy.signal.convolve` have a new optional parameter `method`. The default value of `auto` estimates the fastest of two computation methods, the direct approach and the Fourier transform approach. A new function has been added to choose the convolution/correlation method, `scipy.signal.choose_conv_method` which may be appropriate if convolutions or correlations are performed on many arrays of the same size. New functions have been added to calculate complex short time fourier transforms of an input signal, and to invert the transform to recover the original signal: `scipy.signal.stft` and `scipy.signal.istft`. This implementation also fixes the previously incorrect ouput of `scipy.signal.spectrogram` when complex output data were requested. The function `scipy.signal.sosfreqz` was added to compute the frequency response from second-order sections. The function `scipy.signal.unit_impulse` was added to conveniently generate an impulse function. The function `scipy.signal.iirnotch` was added to design second-order IIR notch filters that can be used to remove a frequency component from a signal. The dual function `scipy.signal.iirpeak` was added to compute the coefficients of a second-order IIR peak (resonant) filter. The function `scipy.signal.minimum_phase` was added to convert linear-phase FIR filters to minimum phase. The functions `scipy.signal.upfirdn` and `scipy.signal.resample_poly` are now substantially faster when operating on some n-dimensional arrays when n > 1. The largest reduction in computation time is realized in cases where the size of the array is small (<1k samples or so) along the axis to be filtered. `scipy.fftpack` improvements - ---------------------------- Fast Fourier transform routines now accept `np.float16` inputs and upcast them to `np.float32`. Previously, they would raise an error. `scipy.cluster` improvements - ---------------------------- Methods ``"centroid"`` and ``"median"`` of `scipy.cluster.hierarchy.linkage` have been significantly sped up. Long-standing issues with using ``linkage`` on large input data (over 16 GB) have been resolved. `scipy.sparse` improvements - --------------------------- The functions `scipy.sparse.save_npz` and `scipy.sparse.load_npz` were added, providing simple serialization for some sparse formats. The `prune` method of classes `bsr_matrix`, `csc_matrix`, and `csr_matrix` was updated to reallocate backing arrays under certain conditions, reducing memory usage. The methods `argmin` and `argmax` were added to classes `coo_matrix`, `csc_matrix`, `csr_matrix`, and `bsr_matrix`. New function `scipy.sparse.csgraph.structural_rank` computes the structural rank of a graph with a given sparsity pattern. New function `scipy.sparse.linalg.spsolve_triangular` solves a sparse linear system with a triangular left hand side matrix. `scipy.special` improvements - ---------------------------- Scalar, typed versions of universal functions from `scipy.special` are available in the Cython space via ``cimport`` from the new module `scipy.special.cython_special`. These scalar functions can be expected to be significantly faster then the universal functions for scalar arguments. See the `scipy.special` tutorial for details. Better control over special-function errors is offered by the functions `scipy.special.geterr` and `scipy.special.seterr` and the context manager `scipy.special.errstate`. The names of orthogonal polynomial root functions have been changed to be consistent with other functions relating to orthogonal polynomials. For example, `scipy.special.j_roots` has been renamed `scipy.special.roots_jacobi` for consistency with the related functions `scipy.special.jacobi` and `scipy.special.eval_jacobi`. To preserve back-compatibility the old names have been left as aliases. Wright Omega function is implemented as `scipy.special.wrightomega`. `scipy.stats` improvements - -------------------------- The function `scipy.stats.weightedtau` was added. It provides a weighted version of Kendall's tau. New class `scipy.stats.multinomial` implements the multinomial distribution. New class `scipy.stats.rv_histogram` constructs a continuous univariate distribution with a piecewise linear CDF from a binned data sample. New class `scipy.stats.argus` implements the Argus distribution. `scipy.interpolate` improvements - -------------------------------- New class `scipy.interpolate.BSpline` represents splines. ``BSpline`` objects contain knots and coefficients and can evaluate the spline. The format is consistent with FITPACK, so that one can do, for example:: >>> t, c, k = splrep(x, y, s=0) >>> spl = BSpline(t, c, k) >>> np.allclose(spl(x), y) ``spl*`` functions, `scipy.interpolate.splev`, `scipy.interpolate.splint`, `scipy.interpolate.splder` and `scipy.interpolate.splantider`, accept both ``BSpline`` objects and ``(t, c, k)`` tuples for backwards compatibility. For multidimensional splines, ``c.ndim > 1``, ``BSpline`` objects are consistent with piecewise polynomials, `scipy.interpolate.PPoly`. This means that ``BSpline`` objects are not immediately consistent with `scipy.interpolate.splprep`, and one *cannot* do ``>>> BSpline(*splprep([x, y])[0])``. Consult the `scipy.interpolate` test suite for examples of the precise equivalence. In new code, prefer using ``scipy.interpolate.BSpline`` objects instead of manipulating ``(t, c, k)`` tuples directly. New function `scipy.interpolate.make_interp_spline` constructs an interpolating spline given data points and boundary conditions. New function `scipy.interpolate.make_lsq_spline` constructs a least-squares spline approximation given data points. `scipy.integrate` improvements - ------------------------------ Now `scipy.integrate.fixed_quad` supports vector-valued functions. Deprecated features =================== `scipy.interpolate.splmake`, `scipy.interpolate.spleval` and `scipy.interpolate.spline` are deprecated. The format used by `splmake/spleval` was inconsistent with `splrep/splev` which was confusing to users. `scipy.special.errprint` is deprecated. Improved functionality is available in `scipy.special.seterr`. calling `scipy.spatial.distance.pdist` or `scipy.spatial.distance.cdist` with arguments not needed by the chosen metric is deprecated. Also, metrics `"old_cosine"` and `"old_cos"` are deprecated. Backwards incompatible changes ============================== The deprecated ``scipy.weave`` submodule was removed. `scipy.spatial.distance.squareform` now returns arrays of the same dtype as the input, instead of always float64. `scipy.special.errprint` now returns a boolean. The function `scipy.signal.find_peaks_cwt` now returns an array instead of a list. `scipy.stats.kendalltau` now computes the correct p-value in case the input contains ties. The p-value is also identical to that computed by `scipy.stats.mstats.kendalltau` and by R. If the input does not contain ties there is no change w.r.t. the previous implementation. The function `scipy.linalg.block_diag` will not ignore zero-sized matrices anymore. Instead it will insert rows or columns of zeros of the appropriate size. See gh-4908 for more details. Other changes ============= SciPy wheels will now report their dependency on ``numpy`` on all platforms. This change was made because Numpy wheels are available, and because the pip upgrade behavior is finally changing for the better (use ``--upgrade-strategy=only-if-needed`` for ``pip >= 8.2``; that behavior will become the default in the next major version of ``pip``). Numerical values returned by `scipy.interpolate.interp1d` with ``kind="cubic"`` and ``"quadratic"`` may change relative to previous scipy versions. If your code depended on specific numeric values (i.e., on implementation details of the interpolators), you may want to double-check your results. Authors ======= * @endolith * Max Argus + * Hervé Audren * Alessandro Pietro Bardelli + * Michael Benfield + * Felix Berkenkamp * Matthew Brett * Per Brodtkorb * Evgeni Burovski * Pierre de Buyl * CJ Carey * Brandon Carter + * Tim Cera * Klesk Chonkin * Christian Häggström + * Luca Citi * Peadar Coyle + * Daniel da Silva + * Greg Dooper + * John Draper + * drlvk + * David Ellis + * Yu Feng * Baptiste Fontaine + * Jed Frey + * Siddhartha Gandhi + * Wim Glenn + * Akash Goel + * Christoph Gohlke * Ralf Gommers * Alexander Goncearenco + * Richard Gowers + * Alex Griffing * Radoslaw Guzinski + * Charles Harris * Callum Jacob Hays + * Ian Henriksen * Randy Heydon + * Lindsey Hiltner + * Gerrit Holl + * Hiroki IKEDA + * jfinkels + * Mher Kazandjian + * Thomas Keck + * keuj6 + * Kornel Kielczewski + * Sergey B Kirpichev + * Vasily Kokorev + * Eric Larson * Denis Laxalde * Gregory R. Lee * Josh Lefler + * Julien Lhermitte + * Evan Limanto + * Jin-Guo Liu + * Nikolay Mayorov * Geordie McBain + * Josue Melka + * Matthieu Melot * michaelvmartin15 + * Surhud More + * Brett M. Morris + * Chris Mutel + * Paul Nation * Andrew Nelson * David Nicholson + * Aaron Nielsen + * Joel Nothman * nrnrk + * Juan Nunez-Iglesias * Mikhail Pak + * Gavin Parnaby + * Thomas Pingel + * Ilhan Polat + * Aman Pratik + * Sebastian Pucilowski * Ted Pudlik * puenka + * Eric Quintero * Tyler Reddy * Joscha Reimer * Antonio Horta Ribeiro + * Edward Richards + * Roman Ring + * Rafael Rossi + * Colm Ryan + * Sami Salonen + * Alvaro Sanchez-Gonzalez + * Johannes Schmitz * Kari Schoonbee * Yurii Shevchuk + * Jonathan Siebert + * Jonathan Tammo Siebert + * Scott Sievert + * Sourav Singh * Byron Smith + * Srikiran + * Samuel St-Jean + * Yoni Teitelbaum + * Bhavika Tekwani * Martin Thoma * timbalam + * Svend Vanderveken + * Sebastiano Vigna + * Aditya Vijaykumar + * Santi Villalba + * Ze Vinicius * Pauli Virtanen * Matteo Visconti * Yusuke Watanabe + * Warren Weckesser * Phillip Weinberg + * Nils Werner * Jakub Wilk * Josh Wilson * wirew0rm + * David Wolever + * Nathan Woods * ybeltukov + * G Young * Evgeny Zhurko + 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. Issues closed for 0.19.0 - ------------------------ - - `#1767 <https://github.com/scipy/scipy/issues/1767>`__: Function definitions in __fitpack.h should be moved. (Trac #1240) - - `#1774 <https://github.com/scipy/scipy/issues/1774>`__: _kmeans chokes on large thresholds (Trac #1247) - - `#2089 <https://github.com/scipy/scipy/issues/2089>`__: Integer overflows cause segfault in linkage function with large... - - `#2190 <https://github.com/scipy/scipy/issues/2190>`__: Are odd-length window functions supposed to be always symmetrical?... - - `#2251 <https://github.com/scipy/scipy/issues/2251>`__: solve_discrete_are in scipy.linalg does (sometimes) not solve... - - `#2580 <https://github.com/scipy/scipy/issues/2580>`__: scipy.interpolate.UnivariateSpline (or a new superclass of it)... - - `#2592 <https://github.com/scipy/scipy/issues/2592>`__: scipy.stats.anderson assumes gumbel_l - - `#3054 <https://github.com/scipy/scipy/issues/3054>`__: scipy.linalg.eig does not handle infinite eigenvalues - - `#3160 <https://github.com/scipy/scipy/issues/3160>`__: multinomial pmf / logpmf - - `#3904 <https://github.com/scipy/scipy/issues/3904>`__: scipy.special.ellipj dn wrong values at quarter period - - `#4044 <https://github.com/scipy/scipy/issues/4044>`__: Inconsistent code book initialization in kmeans - - `#4234 <https://github.com/scipy/scipy/issues/4234>`__: scipy.signal.flattop documentation doesn't list a source for... - - `#4831 <https://github.com/scipy/scipy/issues/4831>`__: Bugs in C code in __quadpack.h - - `#4908 <https://github.com/scipy/scipy/issues/4908>`__: bug: unnessesary validity check for block dimension in scipy.sparse.block_diag - - `#4917 <https://github.com/scipy/scipy/issues/4917>`__: BUG: indexing error for sparse matrix with ix_ - - `#4938 <https://github.com/scipy/scipy/issues/4938>`__: Docs on extending ndimage need to be updated. - - `#5056 <https://github.com/scipy/scipy/issues/5056>`__: sparse matrix element-wise multiplying dense matrix returns dense... - - `#5337 <https://github.com/scipy/scipy/issues/5337>`__: Formula in documentation for correlate is wrong - - `#5537 <https://github.com/scipy/scipy/issues/5537>`__: use OrderedDict in io.netcdf - - `#5750 <https://github.com/scipy/scipy/issues/5750>`__: [doc] missing data index value in KDTree, cKDTree - - `#5755 <https://github.com/scipy/scipy/issues/5755>`__: p-value computation in scipy.stats.kendalltau() in broken in... - - `#5757 <https://github.com/scipy/scipy/issues/5757>`__: BUG: Incorrect complex output of signal.spectrogram - - `#5964 <https://github.com/scipy/scipy/issues/5964>`__: ENH: expose scalar versions of scipy.special functions to cython - - `#6107 <https://github.com/scipy/scipy/issues/6107>`__: scipy.cluster.hierarchy.single segmentation fault with 2**16... - - `#6278 <https://github.com/scipy/scipy/issues/6278>`__: optimize.basinhopping should take a RandomState object - - `#6296 <https://github.com/scipy/scipy/issues/6296>`__: InterpolatedUnivariateSpline: check_finite fails when w is unspecified - - `#6306 <https://github.com/scipy/scipy/issues/6306>`__: Anderson-Darling bad results - - `#6314 <https://github.com/scipy/scipy/issues/6314>`__: scipy.stats.kendaltau() p value not in agreement with R, SPSS... - - `#6340 <https://github.com/scipy/scipy/issues/6340>`__: Curve_fit bounds and maxfev - - `#6377 <https://github.com/scipy/scipy/issues/6377>`__: expm_multiply, complex matrices not working using start,stop,ect... - - `#6382 <https://github.com/scipy/scipy/issues/6382>`__: optimize.differential_evolution stopping criterion has unintuitive... - - `#6391 <https://github.com/scipy/scipy/issues/6391>`__: Global Benchmarking times out at 600s. - - `#6397 <https://github.com/scipy/scipy/issues/6397>`__: mmwrite errors with large (but still 64-bit) integers - - `#6413 <https://github.com/scipy/scipy/issues/6413>`__: scipy.stats.dirichlet computes multivariate gaussian differential... - - `#6428 <https://github.com/scipy/scipy/issues/6428>`__: scipy.stats.mstats.mode modifies input - - `#6440 <https://github.com/scipy/scipy/issues/6440>`__: Figure out ABI break policy for scipy.special Cython API - - `#6441 <https://github.com/scipy/scipy/issues/6441>`__: Using Qhull for halfspace intersection : segfault - - `#6442 <https://github.com/scipy/scipy/issues/6442>`__: scipy.spatial : In incremental mode volume is not recomputed - - `#6451 <https://github.com/scipy/scipy/issues/6451>`__: Documentation for scipy.cluster.hierarchy.to_tree is confusing... - - `#6490 <https://github.com/scipy/scipy/issues/6490>`__: interp1d (kind=zero) returns wrong value for rightmost interpolation... - - `#6521 <https://github.com/scipy/scipy/issues/6521>`__: scipy.stats.entropy does *not* calculate the KL divergence - - `#6530 <https://github.com/scipy/scipy/issues/6530>`__: scipy.stats.spearmanr unexpected NaN handling - - `#6541 <https://github.com/scipy/scipy/issues/6541>`__: Test runner does not run scipy._lib/tests? - - `#6552 <https://github.com/scipy/scipy/issues/6552>`__: BUG: misc.bytescale returns unexpected results when using cmin/cmax... - - `#6556 <https://github.com/scipy/scipy/issues/6556>`__: RectSphereBivariateSpline(u, v, r) fails if min(v) >= pi - - `#6559 <https://github.com/scipy/scipy/issues/6559>`__: Differential_evolution maxiter causing memory overflow - - `#6565 <https://github.com/scipy/scipy/issues/6565>`__: Coverage of spectral functions could be improved - - `#6628 <https://github.com/scipy/scipy/issues/6628>`__: Incorrect parameter name in binomial documentation - - `#6634 <https://github.com/scipy/scipy/issues/6634>`__: Expose LAPACK's xGESVX family for linalg.solve ill-conditioned... - - `#6657 <https://github.com/scipy/scipy/issues/6657>`__: Confusing documentation for `scipy.special.sph_harm` - - `#6676 <https://github.com/scipy/scipy/issues/6676>`__: optimize: Incorrect size of Jacobian returned by `minimize(...,... - - `#6681 <https://github.com/scipy/scipy/issues/6681>`__: add a new context manager to wrap `scipy.special.seterr` - - `#6700 <https://github.com/scipy/scipy/issues/6700>`__: BUG: scipy.io.wavfile.read stays in infinite loop, warns on wav... - - `#6721 <https://github.com/scipy/scipy/issues/6721>`__: scipy.special.chebyt(N) throw a 'TypeError' when N > 64 - - `#6727 <https://github.com/scipy/scipy/issues/6727>`__: Documentation for scipy.stats.norm.fit is incorrect - - `#6764 <https://github.com/scipy/scipy/issues/6764>`__: Documentation for scipy.spatial.Delaunay is partially incorrect - - `#6811 <https://github.com/scipy/scipy/issues/6811>`__: scipy.spatial.SphericalVoronoi fails for large number of points - - `#6841 <https://github.com/scipy/scipy/issues/6841>`__: spearmanr fails when nan_policy='omit' is set - - `#6869 <https://github.com/scipy/scipy/issues/6869>`__: Currently in gaussian_kde, the logpdf function is calculated... - - `#6875 <https://github.com/scipy/scipy/issues/6875>`__: SLSQP inconsistent handling of invalid bounds - - `#6876 <https://github.com/scipy/scipy/issues/6876>`__: Python stopped working (Segfault?) with minimum/maximum filter... - - `#6889 <https://github.com/scipy/scipy/issues/6889>`__: dblquad gives different results under scipy 0.17.1 and 0.18.1 - - `#6898 <https://github.com/scipy/scipy/issues/6898>`__: BUG: dblquad ignores error tolerances - - `#6901 <https://github.com/scipy/scipy/issues/6901>`__: Solving sparse linear systems in CSR format with complex values - - `#6903 <https://github.com/scipy/scipy/issues/6903>`__: issue in spatial.distance.pdist docstring - - `#6917 <https://github.com/scipy/scipy/issues/6917>`__: Problem in passing drop_rule to scipy.sparse.linalg.spilu - - `#6926 <https://github.com/scipy/scipy/issues/6926>`__: signature mismatches for LowLevelCallable - - `#6961 <https://github.com/scipy/scipy/issues/6961>`__: Scipy contains shebang pointing to /usr/bin/python and /bin/bash... - - `#6972 <https://github.com/scipy/scipy/issues/6972>`__: BUG: special: `generate_ufuncs.py` is broken - - `#6984 <https://github.com/scipy/scipy/issues/6984>`__: Assert raises test failure for test_ill_condition_warning - - `#6990 <https://github.com/scipy/scipy/issues/6990>`__: BUG: sparse: Bad documentation of the `k` argument in `sparse.linalg.eigs` - - `#6991 <https://github.com/scipy/scipy/issues/6991>`__: Division by zero in linregress() - - `#7011 <https://github.com/scipy/scipy/issues/7011>`__: possible speed improvment in rv_continuous.fit() - - `#7015 <https://github.com/scipy/scipy/issues/7015>`__: Test failure with Python 3.5 and numpy master - - `#7055 <https://github.com/scipy/scipy/issues/7055>`__: SciPy 0.19.0rc1 test errors and failures on Windows - - `#7096 <https://github.com/scipy/scipy/issues/7096>`__: macOS test failues for test_solve_continuous_are - - `#7100 <https://github.com/scipy/scipy/issues/7100>`__: test_distance.test_Xdist_deprecated_args test error in 0.19.0rc2 Pull requests for 0.19.0 - ------------------------ - - `#2908 <https://github.com/scipy/scipy/pull/2908>`__: Scipy 1.0 Roadmap - - `#3174 <https://github.com/scipy/scipy/pull/3174>`__: add b-splines - - `#4606 <https://github.com/scipy/scipy/pull/4606>`__: ENH: Add a unit impulse waveform function - - `#5608 <https://github.com/scipy/scipy/pull/5608>`__: Adds keyword argument to choose faster convolution method - - `#5647 <https://github.com/scipy/scipy/pull/5647>`__: ENH: Faster count_neighour in cKDTree / + weighted input data - - `#6021 <https://github.com/scipy/scipy/pull/6021>`__: Netcdf append - - `#6058 <https://github.com/scipy/scipy/pull/6058>`__: ENH: scipy.signal - Add stft and istft - - `#6059 <https://github.com/scipy/scipy/pull/6059>`__: ENH: More accurate signal.freqresp for zpk systems - - `#6195 <https://github.com/scipy/scipy/pull/6195>`__: ENH: Cython interface for special - - `#6234 <https://github.com/scipy/scipy/pull/6234>`__: DOC: Fixed a typo in ward() help - - `#6261 <https://github.com/scipy/scipy/pull/6261>`__: ENH: add docstring and clean up code for signal.normalize - - `#6270 <https://github.com/scipy/scipy/pull/6270>`__: MAINT: special: add tests for cdflib - - `#6271 <https://github.com/scipy/scipy/pull/6271>`__: Fix for scipy.cluster.hierarchy.is_isomorphic - - `#6273 <https://github.com/scipy/scipy/pull/6273>`__: optimize: rewrite while loops as for loops - - `#6279 <https://github.com/scipy/scipy/pull/6279>`__: MAINT: Bessel tweaks - - `#6291 <https://github.com/scipy/scipy/pull/6291>`__: Fixes gh-6219: remove runtime warning from genextreme distribution - - `#6294 <https://github.com/scipy/scipy/pull/6294>`__: STY: Some PEP8 and cleaning up imports in stats/_continuous_distns.py - - `#6297 <https://github.com/scipy/scipy/pull/6297>`__: Clarify docs in misc/__init__.py - - `#6300 <https://github.com/scipy/scipy/pull/6300>`__: ENH: sparse: Loosen input validation for `diags` with empty inputs - - `#6301 <https://github.com/scipy/scipy/pull/6301>`__: BUG: standardizes check_finite behavior re optional weights,... - - `#6303 <https://github.com/scipy/scipy/pull/6303>`__: Fixing example in _lazyselect docstring. - - `#6307 <https://github.com/scipy/scipy/pull/6307>`__: MAINT: more improvements to gammainc/gammaincc - - `#6308 <https://github.com/scipy/scipy/pull/6308>`__: Clarified documentation of hypergeometric distribution. - - `#6309 <https://github.com/scipy/scipy/pull/6309>`__: BUG: stats: Improve calculation of the Anderson-Darling statistic. - - `#6315 <https://github.com/scipy/scipy/pull/6315>`__: ENH: Descending order of x in PPoly - - `#6317 <https://github.com/scipy/scipy/pull/6317>`__: ENH: stats: Add support for nan_policy to stats.median_test - - `#6321 <https://github.com/scipy/scipy/pull/6321>`__: TST: fix a typo in test name - - `#6328 <https://github.com/scipy/scipy/pull/6328>`__: ENH: sosfreqz - - `#6335 <https://github.com/scipy/scipy/pull/6335>`__: Define LinregressResult outside of linregress - - `#6337 <https://github.com/scipy/scipy/pull/6337>`__: In anderson test, added support for right skewed gumbel distribution. - - `#6341 <https://github.com/scipy/scipy/pull/6341>`__: Accept several spellings for the curve_fit max number of function... - - `#6342 <https://github.com/scipy/scipy/pull/6342>`__: DOC: cluster: clarify hierarchy.linkage usage - - `#6352 <https://github.com/scipy/scipy/pull/6352>`__: DOC: removed brentq from its own 'see also' - - `#6362 <https://github.com/scipy/scipy/pull/6362>`__: ENH: stats: Use explicit formulas for sf, logsf, etc in weibull... - - `#6369 <https://github.com/scipy/scipy/pull/6369>`__: MAINT: special: add a comment to hyp0f1_complex - - `#6375 <https://github.com/scipy/scipy/pull/6375>`__: Added the multinomial distribution. - - `#6387 <https://github.com/scipy/scipy/pull/6387>`__: MAINT: special: improve accuracy of ellipj's `dn` at quarter... - - `#6388 <https://github.com/scipy/scipy/pull/6388>`__: BenchmarkGlobal - getting it to work in Python3 - - `#6394 <https://github.com/scipy/scipy/pull/6394>`__: ENH: scipy.sparse: add save and load functions for sparse matrices - - `#6400 <https://github.com/scipy/scipy/pull/6400>`__: MAINT: moves global benchmark run from setup_cache to track_all - - `#6403 <https://github.com/scipy/scipy/pull/6403>`__: ENH: seed kwd for basinhopping. Closes #6278 - - `#6404 <https://github.com/scipy/scipy/pull/6404>`__: ENH: signal: added irrnotch and iirpeak functions. - - `#6406 <https://github.com/scipy/scipy/pull/6406>`__: ENH: special: extend `sici`/`shichi` to complex arguments - - `#6407 <https://github.com/scipy/scipy/pull/6407>`__: ENH: Window functions should not accept non-integer or negative... - - `#6408 <https://github.com/scipy/scipy/pull/6408>`__: MAINT: _differentialevolution now uses _lib._util.check_random_state - - `#6427 <https://github.com/scipy/scipy/pull/6427>`__: MAINT: Fix gmpy build & test that mpmath uses gmpy - - `#6439 <https://github.com/scipy/scipy/pull/6439>`__: MAINT: ndimage: update callback function c api - - `#6443 <https://github.com/scipy/scipy/pull/6443>`__: BUG: Fix volume computation in incremental mode - - `#6447 <https://github.com/scipy/scipy/pull/6447>`__: Fixes issue #6413 - Minor documentation fix in the entropy function... - - `#6448 <https://github.com/scipy/scipy/pull/6448>`__: ENH: Add halfspace mode to Qhull - - `#6449 <https://github.com/scipy/scipy/pull/6449>`__: ENH: rtol and atol for differential_evolution termination fixes... - - `#6453 <https://github.com/scipy/scipy/pull/6453>`__: DOC: Add some See Also links between similar functions - - `#6454 <https://github.com/scipy/scipy/pull/6454>`__: DOC: linalg: clarify callable signature in `ordqz` - - `#6457 <https://github.com/scipy/scipy/pull/6457>`__: ENH: spatial: enable non-double dtypes in squareform - - `#6459 <https://github.com/scipy/scipy/pull/6459>`__: BUG: Complex matrices not handled correctly by expm_multiply... - - `#6465 <https://github.com/scipy/scipy/pull/6465>`__: TST DOC Window docs, tests, etc. - - `#6469 <https://github.com/scipy/scipy/pull/6469>`__: ENH: linalg: better handling of infinite eigenvalues in `eig`/`eigvals` - - `#6475 <https://github.com/scipy/scipy/pull/6475>`__: DOC: calling interp1d/interp2d with NaNs is undefined - - `#6477 <https://github.com/scipy/scipy/pull/6477>`__: Document magic numbers in optimize.py - - `#6481 <https://github.com/scipy/scipy/pull/6481>`__: TST: Supress some warnings from test_windows - - `#6485 <https://github.com/scipy/scipy/pull/6485>`__: DOC: spatial: correct typo in procrustes - - `#6487 <https://github.com/scipy/scipy/pull/6487>`__: Fix Bray-Curtis formula in pdist docstring - - `#6493 <https://github.com/scipy/scipy/pull/6493>`__: ENH: Add covariance functionality to scipy.optimize.curve_fit - - `#6494 <https://github.com/scipy/scipy/pull/6494>`__: ENH: stats: Use log1p() to improve some calculations. - - `#6495 <https://github.com/scipy/scipy/pull/6495>`__: BUG: Use MST algorithm instead of SLINK for single linkage clustering - - `#6497 <https://github.com/scipy/scipy/pull/6497>`__: MRG: Add minimum_phase filter function - - `#6505 <https://github.com/scipy/scipy/pull/6505>`__: reset scipy.signal.resample window shape to 1-D - - `#6507 <https://github.com/scipy/scipy/pull/6507>`__: BUG: linkage: Raise exception if y contains non-finite elements - - `#6509 <https://github.com/scipy/scipy/pull/6509>`__: ENH: _lib: add common machinery for low-level callback functions - - `#6520 <https://github.com/scipy/scipy/pull/6520>`__: scipy.sparse.base.__mul__ non-numpy/scipy objects with 'shape'... - - `#6522 <https://github.com/scipy/scipy/pull/6522>`__: Replace kl_div by rel_entr in entropy - - `#6524 <https://github.com/scipy/scipy/pull/6524>`__: DOC: add next_fast_len to list of functions - - `#6527 <https://github.com/scipy/scipy/pull/6527>`__: DOC: Release notes to reflect the new covariance feature in optimize.curve_fit - - `#6532 <https://github.com/scipy/scipy/pull/6532>`__: ENH: Simplify _cos_win, document it, add symmetric/periodic arg - - `#6535 <https://github.com/scipy/scipy/pull/6535>`__: MAINT: sparse.csgraph: updating old cython loops - - `#6540 <https://github.com/scipy/scipy/pull/6540>`__: DOC: add to documentation of orthogonal polynomials - - `#6544 <https://github.com/scipy/scipy/pull/6544>`__: TST: Ensure tests for scipy._lib are run by scipy.test() - - `#6546 <https://github.com/scipy/scipy/pull/6546>`__: updated docstring of stats.linregress - - `#6553 <https://github.com/scipy/scipy/pull/6553>`__: commited changes that I originally submitted for scipy.signal.cspline… - - `#6561 <https://github.com/scipy/scipy/pull/6561>`__: BUG: modify signal.find_peaks_cwt() to return array and accept... - - `#6562 <https://github.com/scipy/scipy/pull/6562>`__: DOC: Negative binomial distribution clarification - - `#6563 <https://github.com/scipy/scipy/pull/6563>`__: MAINT: be more liberal in requiring numpy - - `#6567 <https://github.com/scipy/scipy/pull/6567>`__: MAINT: use xrange for iteration in differential_evolution fixes... - - `#6572 <https://github.com/scipy/scipy/pull/6572>`__: BUG: "sp.linalg.solve_discrete_are" fails for random data - - `#6578 <https://github.com/scipy/scipy/pull/6578>`__: BUG: misc: allow both cmin/cmax and low/high params in bytescale - - `#6581 <https://github.com/scipy/scipy/pull/6581>`__: Fix some unfortunate typos - - `#6582 <https://github.com/scipy/scipy/pull/6582>`__: MAINT: linalg: make handling of infinite eigenvalues in `ordqz`... - - `#6585 <https://github.com/scipy/scipy/pull/6585>`__: DOC: interpolate: correct seealso links to ndimage - - `#6588 <https://github.com/scipy/scipy/pull/6588>`__: Update docstring of scipy.spatial.distance_matrix - - `#6592 <https://github.com/scipy/scipy/pull/6592>`__: DOC: Replace 'first' by 'smallest' in mode - - `#6593 <https://github.com/scipy/scipy/pull/6593>`__: MAINT: remove scipy.weave submodule - - `#6594 <https://github.com/scipy/scipy/pull/6594>`__: DOC: distance.squareform: fix html docs, add note about dtype... - - `#6598 <https://github.com/scipy/scipy/pull/6598>`__: [DOC] Fix incorrect error message in medfilt2d - - `#6599 <https://github.com/scipy/scipy/pull/6599>`__: MAINT: linalg: turn a `solve_discrete_are` test back on - - `#6600 <https://github.com/scipy/scipy/pull/6600>`__: DOC: Add SOS goals to roadmap - - `#6601 <https://github.com/scipy/scipy/pull/6601>`__: DEP: Raise minimum numpy version to 1.8.2 - - `#6605 <https://github.com/scipy/scipy/pull/6605>`__: MAINT: 'new' module is deprecated, don't use it - - `#6607 <https://github.com/scipy/scipy/pull/6607>`__: DOC: add note on change in wheel dependency on numpy and pip... - - `#6609 <https://github.com/scipy/scipy/pull/6609>`__: Fixes #6602 - Typo in docs - - `#6616 <https://github.com/scipy/scipy/pull/6616>`__: ENH: generalization of continuous and discrete Riccati solvers... - - `#6621 <https://github.com/scipy/scipy/pull/6621>`__: DOC: improve cluster.hierarchy docstrings. - - `#6623 <https://github.com/scipy/scipy/pull/6623>`__: CS matrix prune method should copy data from large unpruned arrays - - `#6625 <https://github.com/scipy/scipy/pull/6625>`__: DOC: special: complete documentation of `eval_*` functions - - `#6626 <https://github.com/scipy/scipy/pull/6626>`__: TST: special: silence some deprecation warnings - - `#6631 <https://github.com/scipy/scipy/pull/6631>`__: fix parameter name doc for discrete distributions - - `#6632 <https://github.com/scipy/scipy/pull/6632>`__: MAINT: stats: change some instances of `special` to `sc` - - `#6633 <https://github.com/scipy/scipy/pull/6633>`__: MAINT: refguide: py2k long integers are equal to py3k integers - - `#6638 <https://github.com/scipy/scipy/pull/6638>`__: MAINT: change type declaration in cluster.linkage, prevent overflow - - `#6640 <https://github.com/scipy/scipy/pull/6640>`__: BUG: fix issue with duplicate values used in cluster.vq.kmeans - - `#6641 <https://github.com/scipy/scipy/pull/6641>`__: BUG: fix corner case in cluster.vq.kmeans for large thresholds - - `#6643 <https://github.com/scipy/scipy/pull/6643>`__: MAINT: clean up truncation modes of dendrogram - - `#6645 <https://github.com/scipy/scipy/pull/6645>`__: MAINT: special: rename `*_roots` functions - - `#6646 <https://github.com/scipy/scipy/pull/6646>`__: MAINT: clean up mpmath imports - - `#6647 <https://github.com/scipy/scipy/pull/6647>`__: DOC: add sqrt to Mahalanobis description for pdist - - `#6648 <https://github.com/scipy/scipy/pull/6648>`__: DOC: special: add a section on `cython_special` to the tutorial - - `#6649 <https://github.com/scipy/scipy/pull/6649>`__: ENH: Added scipy.spatial.distance.directed_hausdorff - - `#6650 <https://github.com/scipy/scipy/pull/6650>`__: DOC: add Sphinx roles for DOI and arXiv links - - `#6651 <https://github.com/scipy/scipy/pull/6651>`__: BUG: mstats: make sure mode(..., None) does not modify its input - - `#6652 <https://github.com/scipy/scipy/pull/6652>`__: DOC: special: add section to tutorial on functions not in special - - `#6653 <https://github.com/scipy/scipy/pull/6653>`__: ENH: special: add the Wright Omega function - - `#6656 <https://github.com/scipy/scipy/pull/6656>`__: ENH: don't coerce input to double with custom metric in cdist... - - `#6658 <https://github.com/scipy/scipy/pull/6658>`__: Faster/shorter code for computation of discordances - - `#6659 <https://github.com/scipy/scipy/pull/6659>`__: DOC: special: make __init__ summaries and html summaries match - - `#6661 <https://github.com/scipy/scipy/pull/6661>`__: general.rst: Fix a typo - - `#6664 <https://github.com/scipy/scipy/pull/6664>`__: TST: Spectral functions' window correction factor - - `#6665 <https://github.com/scipy/scipy/pull/6665>`__: [DOC] Conditions on v in RectSphereBivariateSpline - - `#6668 <https://github.com/scipy/scipy/pull/6668>`__: DOC: Mention negative masses for center of mass - - `#6675 <https://github.com/scipy/scipy/pull/6675>`__: MAINT: special: remove outdated README - - `#6677 <https://github.com/scipy/scipy/pull/6677>`__: BUG: Fixes computation of p-values. - - `#6679 <https://github.com/scipy/scipy/pull/6679>`__: BUG: optimize: return correct Jacobian for method 'SLSQP' in... - - `#6680 <https://github.com/scipy/scipy/pull/6680>`__: ENH: Add structural rank to sparse.csgraph - - `#6686 <https://github.com/scipy/scipy/pull/6686>`__: TST: Added Airspeed Velocity benchmarks for SphericalVoronoi - - `#6687 <https://github.com/scipy/scipy/pull/6687>`__: DOC: add section "deciding on new features" to developer guide. - - `#6691 <https://github.com/scipy/scipy/pull/6691>`__: ENH: Clearer error when fmin_slsqp obj doesn't return scalar - - `#6702 <https://github.com/scipy/scipy/pull/6702>`__: TST: Added airspeed velocity benchmarks for scipy.spatial.distance.cdist - - `#6707 <https://github.com/scipy/scipy/pull/6707>`__: TST: interpolate: test fitpack wrappers, not _impl - - `#6709 <https://github.com/scipy/scipy/pull/6709>`__: TST: fix a number of test failures on 32-bit systems - - `#6711 <https://github.com/scipy/scipy/pull/6711>`__: MAINT: move function definitions from __fitpack.h to _fitpackmodule.c - - `#6712 <https://github.com/scipy/scipy/pull/6712>`__: MAINT: clean up wishlist in stats.morestats, and copyright statement. - - `#6715 <https://github.com/scipy/scipy/pull/6715>`__: DOC: update the release notes with BSpline et al. - - `#6716 <https://github.com/scipy/scipy/pull/6716>`__: MAINT: scipy.io.wavfile: No infinite loop when trying to read... - - `#6717 <https://github.com/scipy/scipy/pull/6717>`__: some style cleanup - - `#6723 <https://github.com/scipy/scipy/pull/6723>`__: BUG: special: cast to float before in-place multiplication in... - - `#6726 <https://github.com/scipy/scipy/pull/6726>`__: address performance regressions in interp1d - - `#6728 <https://github.com/scipy/scipy/pull/6728>`__: DOC: made code examples in `integrate` tutorial copy-pasteable - - `#6731 <https://github.com/scipy/scipy/pull/6731>`__: DOC: scipy.optimize: Added an example for wrapping complex-valued... - - `#6732 <https://github.com/scipy/scipy/pull/6732>`__: MAINT: cython_special: remove `errprint` - - `#6733 <https://github.com/scipy/scipy/pull/6733>`__: MAINT: special: fix some pyflakes warnings - - `#6734 <https://github.com/scipy/scipy/pull/6734>`__: DOC: sparse.linalg: fixed matrix description in `bicgstab` doc - - `#6737 <https://github.com/scipy/scipy/pull/6737>`__: BLD: update `cythonize.py` to detect changes in pxi files - - `#6740 <https://github.com/scipy/scipy/pull/6740>`__: DOC: special: some small fixes to docstrings - - `#6741 <https://github.com/scipy/scipy/pull/6741>`__: MAINT: remove dead code in interpolate.py - - `#6742 <https://github.com/scipy/scipy/pull/6742>`__: BUG: fix ``linalg.block_diag`` to support zero-sized matrices. - - `#6744 <https://github.com/scipy/scipy/pull/6744>`__: ENH: interpolate: make PPoly.from_spline accept BSpline objects - - `#6746 <https://github.com/scipy/scipy/pull/6746>`__: DOC: special: clarify use of Condon-Shortley phase in `sph_harm`/`lpmv` - - `#6750 <https://github.com/scipy/scipy/pull/6750>`__: ENH: sparse: avoid densification on broadcasted elem-wise mult - - `#6751 <https://github.com/scipy/scipy/pull/6751>`__: sinm doc explained cosm - - `#6753 <https://github.com/scipy/scipy/pull/6753>`__: ENH: special: allow for more fine-tuned error handling - - `#6759 <https://github.com/scipy/scipy/pull/6759>`__: Move logsumexp and pade from scipy.misc to scipy.special and... - - `#6761 <https://github.com/scipy/scipy/pull/6761>`__: ENH: argmax and argmin methods for sparse matrices - - `#6762 <https://github.com/scipy/scipy/pull/6762>`__: DOC: Improve docstrings of sparse matrices - - `#6763 <https://github.com/scipy/scipy/pull/6763>`__: ENH: Weighted tau - - `#6768 <https://github.com/scipy/scipy/pull/6768>`__: ENH: cythonized spherical Voronoi region polygon vertex sorting - - `#6770 <https://github.com/scipy/scipy/pull/6770>`__: Correction of Delaunay class' documentation - - `#6775 <https://github.com/scipy/scipy/pull/6775>`__: ENH: Integrating LAPACK "expert" routines with conditioning warnings... - - `#6776 <https://github.com/scipy/scipy/pull/6776>`__: MAINT: Removing the trivial f2py warnings - - `#6777 <https://github.com/scipy/scipy/pull/6777>`__: DOC: Update rv_continuous.fit doc. - - `#6778 <https://github.com/scipy/scipy/pull/6778>`__: MAINT: cluster.hierarchy: Improved wording of error msgs - - `#6786 <https://github.com/scipy/scipy/pull/6786>`__: BLD: increase minimum Cython version to 0.23.4 - - `#6787 <https://github.com/scipy/scipy/pull/6787>`__: DOC: expand on ``linalg.block_diag`` changes in 0.19.0 release... - - `#6789 <https://github.com/scipy/scipy/pull/6789>`__: ENH: Add further documentation for norm.fit - - `#6790 <https://github.com/scipy/scipy/pull/6790>`__: MAINT: Fix a potential problem in nn_chain linkage algorithm - - `#6791 <https://github.com/scipy/scipy/pull/6791>`__: DOC: Add examples to scipy.ndimage.fourier - - `#6792 <https://github.com/scipy/scipy/pull/6792>`__: DOC: fix some numpydoc / Sphinx issues. - - `#6793 <https://github.com/scipy/scipy/pull/6793>`__: MAINT: fix circular import after moving functions out of misc - - `#6796 <https://github.com/scipy/scipy/pull/6796>`__: TST: test importing each submodule. Regression test for gh-6793. - - `#6799 <https://github.com/scipy/scipy/pull/6799>`__: ENH: stats: Argus distribution - - `#6801 <https://github.com/scipy/scipy/pull/6801>`__: ENH: stats: Histogram distribution - - `#6803 <https://github.com/scipy/scipy/pull/6803>`__: TST: make sure tests for ``_build_utils`` are run. - - `#6804 <https://github.com/scipy/scipy/pull/6804>`__: MAINT: more fixes in `loggamma` - - `#6806 <https://github.com/scipy/scipy/pull/6806>`__: ENH: Faster linkage for 'centroid' and 'median' methods - - `#6810 <https://github.com/scipy/scipy/pull/6810>`__: ENH: speed up upfirdn and resample_poly for n-dimensional arrays - - `#6812 <https://github.com/scipy/scipy/pull/6812>`__: TST: Added ConvexHull asv benchmark code - - `#6814 <https://github.com/scipy/scipy/pull/6814>`__: ENH: Different extrapolation modes for different dimensions in... - - `#6826 <https://github.com/scipy/scipy/pull/6826>`__: Signal spectral window default fix - - `#6828 <https://github.com/scipy/scipy/pull/6828>`__: BUG: SphericalVoronoi Space Complexity (Fixes #6811) - - `#6830 <https://github.com/scipy/scipy/pull/6830>`__: RealData docstring correction - - `#6834 <https://github.com/scipy/scipy/pull/6834>`__: DOC: Added reference for skewtest function. See #6829 - - `#6836 <https://github.com/scipy/scipy/pull/6836>`__: DOC: Added mode='mirror' in the docstring for the functions accepting... - - `#6838 <https://github.com/scipy/scipy/pull/6838>`__: MAINT: sparse: start removing old BSR methods - - `#6844 <https://github.com/scipy/scipy/pull/6844>`__: handle incompatible dimensions when input is not an ndarray in... - - `#6847 <https://github.com/scipy/scipy/pull/6847>`__: Added maxiter to golden search. - - `#6850 <https://github.com/scipy/scipy/pull/6850>`__: BUG: added check for optional param scipy.stats.spearmanr - - `#6858 <https://github.com/scipy/scipy/pull/6858>`__: MAINT: Removing redundant tests - - `#6861 <https://github.com/scipy/scipy/pull/6861>`__: DEP: Fix escape sequences deprecated in Python 3.6. - - `#6862 <https://github.com/scipy/scipy/pull/6862>`__: DOC: dx should be float, not int - - `#6863 <https://github.com/scipy/scipy/pull/6863>`__: updated documentation curve_fit - - `#6866 <https://github.com/scipy/scipy/pull/6866>`__: DOC : added some documentation to j1 referring to spherical_jn - - `#6867 <https://github.com/scipy/scipy/pull/6867>`__: DOC: cdist move long examples list into Notes section - - `#6868 <https://github.com/scipy/scipy/pull/6868>`__: BUG: Make stats.mode return a ModeResult namedtuple on empty... - - `#6871 <https://github.com/scipy/scipy/pull/6871>`__: Corrected documentation. - - `#6874 <https://github.com/scipy/scipy/pull/6874>`__: ENH: gaussian_kde.logpdf based on logsumexp - - `#6877 <https://github.com/scipy/scipy/pull/6877>`__: BUG: ndimage: guard against footprints of all zeros - - `#6881 <https://github.com/scipy/scipy/pull/6881>`__: python 3.6 - - `#6885 <https://github.com/scipy/scipy/pull/6885>`__: Vectorized integrate.fixed_quad - - `#6886 <https://github.com/scipy/scipy/pull/6886>`__: fixed typo - - `#6891 <https://github.com/scipy/scipy/pull/6891>`__: TST: fix failures for linalg.dare/care due to tightened test... - - `#6892 <https://github.com/scipy/scipy/pull/6892>`__: DOC: fix a bunch of Sphinx errors. - - `#6894 <https://github.com/scipy/scipy/pull/6894>`__: TST: Added asv benchmarks for scipy.spatial.Voronoi - - `#6908 <https://github.com/scipy/scipy/pull/6908>`__: BUG: Fix return dtype for complex input in spsolve - - `#6909 <https://github.com/scipy/scipy/pull/6909>`__: ENH: fftpack: use float32 routines for float16 inputs. - - `#6911 <https://github.com/scipy/scipy/pull/6911>`__: added min/max support to binned_statistic - - `#6913 <https://github.com/scipy/scipy/pull/6913>`__: Fix 6875: SLSQP raise ValueError for all invalid bounds. - - `#6914 <https://github.com/scipy/scipy/pull/6914>`__: DOCS: GH6903 updating docs of Spatial.distance.pdist - - `#6916 <https://github.com/scipy/scipy/pull/6916>`__: MAINT: fix some issues for 32-bit Python - - `#6924 <https://github.com/scipy/scipy/pull/6924>`__: BLD: update Bento build for scipy.LowLevelCallable - - `#6932 <https://github.com/scipy/scipy/pull/6932>`__: ENH: Use OrderedDict in io.netcdf. Closes gh-5537 - - `#6933 <https://github.com/scipy/scipy/pull/6933>`__: BUG: fix LowLevelCallable issue on 32-bit Python. - - `#6936 <https://github.com/scipy/scipy/pull/6936>`__: BUG: sparse: handle size-1 2D indexes correctly - - `#6938 <https://github.com/scipy/scipy/pull/6938>`__: TST: fix test failures in special on 32-bit Python. - - `#6939 <https://github.com/scipy/scipy/pull/6939>`__: Added attributes list to cKDTree docstring - - `#6940 <https://github.com/scipy/scipy/pull/6940>`__: improve efficiency of dok_matrix.tocoo - - `#6942 <https://github.com/scipy/scipy/pull/6942>`__: DOC: add link to liac-arff package in the io.arff docstring. - - `#6943 <https://github.com/scipy/scipy/pull/6943>`__: MAINT: Docstring fixes and an additional test for linalg.solve - - `#6944 <https://github.com/scipy/scipy/pull/6944>`__: DOC: Add example of odeint with a banded Jacobian to the integrate... - - `#6946 <https://github.com/scipy/scipy/pull/6946>`__: ENH: hypergeom.logpmf in terms of betaln - - `#6947 <https://github.com/scipy/scipy/pull/6947>`__: TST: speedup distance tests - - `#6948 <https://github.com/scipy/scipy/pull/6948>`__: DEP: Deprecate the keyword "debug" from linalg.solve - - `#6950 <https://github.com/scipy/scipy/pull/6950>`__: BUG: Correctly treat large integers in MMIO (fixes #6397) - - `#6952 <https://github.com/scipy/scipy/pull/6952>`__: ENH: Minor user-friendliness cleanup in LowLevelCallable - - `#6956 <https://github.com/scipy/scipy/pull/6956>`__: DOC: improve description of 'output' keyword for convolve - - `#6957 <https://github.com/scipy/scipy/pull/6957>`__: ENH more informative error in sparse.bmat - - `#6962 <https://github.com/scipy/scipy/pull/6962>`__: Shebang fixes - - `#6964 <https://github.com/scipy/scipy/pull/6964>`__: DOC: note argmin/argmax addition - - `#6965 <https://github.com/scipy/scipy/pull/6965>`__: BUG: Fix issues passing error tolerances in dblquad and tplquad. - - `#6971 <https://github.com/scipy/scipy/pull/6971>`__: fix the docstring of signaltools.correlate - - `#6973 <https://github.com/scipy/scipy/pull/6973>`__: Silence expected numpy warnings in scipy.ndimage.interpolation.zoom() - - `#6975 <https://github.com/scipy/scipy/pull/6975>`__: BUG: special: fix regex in `generate_ufuncs.py` - - `#6976 <https://github.com/scipy/scipy/pull/6976>`__: Update docstring for griddata - - `#6978 <https://github.com/scipy/scipy/pull/6978>`__: Avoid division by zero in zoom factor calculation - - `#6979 <https://github.com/scipy/scipy/pull/6979>`__: BUG: ARE solvers did not check the generalized case carefully - - `#6985 <https://github.com/scipy/scipy/pull/6985>`__: ENH: sparse: add scipy.sparse.linalg.spsolve_triangular - - `#6994 <https://github.com/scipy/scipy/pull/6994>`__: MAINT: spatial: updates to plotting utils - - `#6995 <https://github.com/scipy/scipy/pull/6995>`__: DOC: Bad documentation of k in sparse.linalg.eigs See #6990 - - `#6997 <https://github.com/scipy/scipy/pull/6997>`__: TST: Changed the test with a less singular example - - `#7000 <https://github.com/scipy/scipy/pull/7000>`__: DOC: clarify interp1d 'zero' argument - - `#7007 <https://github.com/scipy/scipy/pull/7007>`__: BUG: Fix division by zero in linregress() for 2 data points - - `#7009 <https://github.com/scipy/scipy/pull/7009>`__: BUG: Fix problem in passing drop_rule to scipy.sparse.linalg.spilu - - `#7012 <https://github.com/scipy/scipy/pull/7012>`__: speed improvment in _distn_infrastructure.py - - `#7014 <https://github.com/scipy/scipy/pull/7014>`__: Fix Typo: add a single quotation mark to fix a slight typo - - `#7021 <https://github.com/scipy/scipy/pull/7021>`__: MAINT: stats: use machine constants from np.finfo, not machar - - `#7026 <https://github.com/scipy/scipy/pull/7026>`__: MAINT: update .mailmap - - `#7032 <https://github.com/scipy/scipy/pull/7032>`__: Fix layout of rv_histogram docs - - `#7035 <https://github.com/scipy/scipy/pull/7035>`__: DOC: update 0.19.0 release notes - - `#7036 <https://github.com/scipy/scipy/pull/7036>`__: ENH: Add more boundary options to signal.stft - - `#7040 <https://github.com/scipy/scipy/pull/7040>`__: TST: stats: skip too slow tests - - `#7042 <https://github.com/scipy/scipy/pull/7042>`__: MAINT: sparse: speed up setdiag tests - - `#7043 <https://github.com/scipy/scipy/pull/7043>`__: MAINT: refactory and code cleaning Xdist - - `#7053 <https://github.com/scipy/scipy/pull/7053>`__: Fix msvc 9 and 10 compile errors - - `#7060 <https://github.com/scipy/scipy/pull/7060>`__: DOC: updated release notes with #7043 and #6656 - - `#7062 <https://github.com/scipy/scipy/pull/7062>`__: MAINT: Change defaut STFT boundary kwarg to "zeros" - - `#7064 <https://github.com/scipy/scipy/pull/7064>`__: Fix ValueError: path is on mount 'X:', start on mount 'D:' on... - - `#7067 <https://github.com/scipy/scipy/pull/7067>`__: TST: Fix PermissionError: [Errno 13] Permission denied on Windows - - `#7068 <https://github.com/scipy/scipy/pull/7068>`__: TST: Fix UnboundLocalError: local variable 'data' referenced... - - `#7069 <https://github.com/scipy/scipy/pull/7069>`__: Fix OverflowError: Python int too large to convert to C long... - - `#7071 <https://github.com/scipy/scipy/pull/7071>`__: TST: silence RuntimeWarning for nan test of stats.spearmanr - - `#7072 <https://github.com/scipy/scipy/pull/7072>`__: Fix OverflowError: Python int too large to convert to C long... - - `#7084 <https://github.com/scipy/scipy/pull/7084>`__: TST: linalg: bump tolerance in test_falker - - `#7095 <https://github.com/scipy/scipy/pull/7095>`__: TST: linalg: bump more tolerances in test_falker - - `#7101 <https://github.com/scipy/scipy/pull/7101>`__: TST: Relax solve_continuous_are test case 2 and 12 - - `#7106 <https://github.com/scipy/scipy/pull/7106>`__: BUG: stop cdist "correlation" modifying input - - `#7116 <https://github.com/scipy/scipy/pull/7116>`__: Backports to 0.19.0rc2 Checksums ========= MD5 ~~~ dde4d5d44a0274a5abb01be4a3cd486a scipy-0.19.0-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl 08809612b46e660e567e3272ec11c808 scipy-0.19.0-cp27-cp27m-manylinux1_i686.whl 0e49f7fc8d31c1c79f0a4d63b29e8a1f scipy-0.19.0-cp27-cp27m-manylinux1_x86_64.whl a2669158cf847856d292b8a60cdaa170 scipy-0.19.0-cp27-cp27mu-manylinux1_i686.whl adfa1f5127a789165dfe9ff140ec0d6e scipy-0.19.0-cp27-cp27mu-manylinux1_x86_64.whl d568c9f60683c33b81ebc1c39eea198a scipy-0.19.0-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl a90148fec477c1950578b40a1197509f scipy-0.19.0-cp34-cp34m-manylinux1_i686.whl ed27be5380e0aaf0229adf747e760f8c scipy-0.19.0-cp34-cp34m-manylinux1_x86_64.whl 4cda63dc7b73bd03bdf9e8ebc6027526 scipy-0.19.0-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl ff8e652b5e918b276793f1ce542a5959 scipy-0.19.0-cp35-cp35m-manylinux1_i686.whl 60741a900a145eb924ec861ec2743582 scipy-0.19.0-cp35-cp35m-manylinux1_x86_64.whl 81685a961d6118459b7787e8465c8d36 scipy-0.19.0-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl 83f0750862c80a659686797d4ec9bca0 scipy-0.19.0-cp36-cp36m-manylinux1_i686.whl 3cbb30615496fbbf9b52c9a643c6fe5e scipy-0.19.0-cp36-cp36m-manylinux1_x86_64.whl 735cdb6fbfcb9917535749816202d0af scipy-0.19.0.tar.gz b21466e87a642940fb9ba35be74940a3 scipy-0.19.0.tar.xz 91b8396231eec780222a57703d3ec550 scipy-0.19.0.zip SHA256 ~~~~~~ 517a85600d6574fef1a67e6d2001b847c27c8bfd136f7a12879c3f91e7bb291f scipy-0.19.0-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl 3c34987ee52fd98b34f2e4a6d277b452d49056f1383550acc54c5bab408a194c scipy-0.19.0-cp27-cp27m-manylinux1_i686.whl eabdfde8276007e9aec9a400f9528645001a30f7d78b04a0ab215183d9523e2a scipy-0.19.0-cp27-cp27m-manylinux1_x86_64.whl fa67bbb0a3225fcd8610d693e7b2ca08fda107359e48229f7b83593bbb70cc97 scipy-0.19.0-cp27-cp27mu-manylinux1_i686.whl 4147b97709e75822e73d312e4d262410baafa961a7b11649a7b4b7c2d41fb4fe scipy-0.19.0-cp27-cp27mu-manylinux1_x86_64.whl 663e78bfa197376547424aff9fb5009e7b2f26855ee5aaf1a2ddbb2f4dc6af3b scipy-0.19.0-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl 2e70ded029d51f6a48d4b2a154f583b85aa2e3290dfd71e0b6bbcfe9454cffdd scipy-0.19.0-cp34-cp34m-manylinux1_i686.whl 4b2731a191dfa48a05b2f5bc18881595a1418092092ecdd8d3feab80f72adc96 scipy-0.19.0-cp34-cp34m-manylinux1_x86_64.whl 57f7be33f1009ad6199132e8a7e5d4c9727224680d8cbc4596a2a8935a86f96b scipy-0.19.0-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl 0496f2b204a63cde3797e5452bf671ee25afc11bd9489ae69cd4dccee13083a1 scipy-0.19.0-cp35-cp35m-manylinux1_i686.whl 1bcf71f2e534a1aabf9f075700701bf3af434120b1b114dfa4723d02e076ed1f scipy-0.19.0-cp35-cp35m-manylinux1_x86_64.whl e1c45905f550b5f14e1f47697c92bab5c1e6ba77da5a441bd2affa4621c41b26 scipy-0.19.0-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl 47f537214293a5d74b05d217afa49b582b7fd9428ec9ea64be69210cfc56611a scipy-0.19.0-cp36-cp36m-manylinux1_i686.whl f5a3a7dcbeb345c227770029870aeb547a3c207a6cbc0106d6157139fd0c23e9 scipy-0.19.0-cp36-cp36m-manylinux1_x86_64.whl eba12b5f757a8c839b26a06f4936ecb80b65cb3674981ee25449b2a55663abe8 scipy-0.19.0.tar.gz ed52232afb2b321a4978e39040d94bf81af90176ba64f58c4499dc305a024437 scipy-0.19.0.tar.xz 4190d34bf9a09626cd42100bbb12e3d96b2daf1a8a3244e991263eb693732122 scipy-0.19.0.zip -----BEGIN PGP SIGNATURE----- Version: GnuPG v1.4.11 (GNU/Linux) iQEcBAEBCAAGBQJYwWscAAoJEIp0pQ0zQcu+JG8H+gOy04ecVl+IBisy/Fz5wfuy xrsCT5yhRPQpxKph8g/6Us5Oh7s8ixrVt9gTVccAspmWXQJhMy5kcppC3s5WsvU+ jFOwUnW7c9QvtIf2ZD9Ay/56WojlXg1ui17MqoCbmkEn2QE8KTKu93hIZpVD5wmV 1fhd7u/ieeQ7sfj6gMZzt0AGpVjnGedEzHRY4zI0PkiCY+Ex8sc2W8G2h5Qbnx9r KoqECIuesLQzVbNgPhWaWaiE1TNX0EJYdWQll0T8scI4opUdg6vEaR05aPhxeR1r KaGEnvfASTZ369COkuVB4JINlKQj0dwLBFIr9NGVzX4vU74GMh5TuDfJlA/mvGU= =bOnA -----END PGP SIGNATURE-----

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