NumPy-Discussion December 2018

numpy-discussion@python.org

25 participants
22 discussions

Re: [Numpy-discussion] example reading binary Fortran file
by Neil Martinsen-Burrell 22 Jul '21

22 Jul '21

David Froger <david.froger.info <at> gmail.com> writes: > Hy,My question is about reading Fortran binary file (oh no this question > again...) I've posted this before, but I finally got it cleaned up for the Cookbook. For this purpose I use a subclass of file that has methods for reading unformatted Fortran data. See http://www.scipy.org/Cookbook/FortranIO/FortranFile. I'd gladly see this in numpy or scipy somewhere, but I'm not sure where it belongs. > program makeArray > implicit none > integer,parameter:: nx=10,ny=20 > real(4),dimension(nx,ny):: ux,uy,p > integer :: i,j > open(11,file='uxuyp.bin',form='unformatted') > do i = 1,nx > do j = 1,ny > ux(i,j) = real(i*j) > uy(i,j) = real(i)/real(j) > p (i,j) = real(i) + real(j) > enddo > enddo > write(11) ux,uy > write(11) p > close(11) > end program makeArray When I run the above program compiled with gfortran on my Intel Mac, I can read it back with:: >>> import numpy as np >>> from fortranfile import FortranFile >>> f=FortranFile('uxuyp.bin', endian='<') >>> uxuy = f.readReals(prec='f') # 'f' for default reals >>> len(uxuy) 400 >>> ux = np.array(uxuy[:200]).reshape((20,10)).T >>> uy = np.array(uxuy[200:]).reshape((20,10)).T >>> p = f.readReals('f').reshape((20,10)).T >>> ux array([[ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20.], [ 2., 4., 6., 8., 10., 12., 14., 16., 18., 20., 22., 24., 26., 28., 30., 32., 34., 36., 38., 40.], [ 3., 6., 9., 12., 15., 18., 21., 24., 27., 30., 33., 36., 39., 42., 45., 48., 51., 54., 57., 60.], [ 4., 8., 12., 16., 20., 24., 28., 32., 36., 40., 44., 48., 52., 56., 60., 64., 68., 72., 76., 80.], [ 5., 10., 15., 20., 25., 30., 35., 40., 45., 50., 55., 60., 65., 70., 75., 80., 85., 90., 95., 100.], [ 6., 12., 18., 24., 30., 36., 42., 48., 54., 60., 66., 72., 78., 84., 90., 96., 102., 108., 114., 120.], [ 7.,Proxy-Connection: keep-alive Cache-Control: max-age=0 14., 21., 28., 35., 42., 49., 56., 63., 70., 77., 84., 91., 98., 105., 112., 119., 126., 133., 140.], [ 8., 16., 24., 32., 40., 48., 56., 64., 72., 80., 88., 96., 104., 112., 120., 128., 136., 144., 152., 160.], [ 9., 18., 27., 36., 45., 54., 63., 72., 81., 90., 99., 108., 117., 126., 135., 144., 153., 162., 171., 180.], [ 10., 20., 30., 40., 50., 60., 70., 80., 90., 100., 110., 120., 130., 140., 150., 160., 170., 180., 190., 200.]]) >>> uy array([[ 1. , 0.5 , 0.33333334, 0.25 , 0.2 , 0.16666667, 0.14285715, 0.125 , 0.11111111, 0.1 , 0.09090909, 0.08333334, 0.07692308, 0.07142857, 0.06666667, 0.0625 , 0.05882353, 0.05555556, 0.05263158, 0.05 ], [ 2. , 1. , 0.66666669, 0.5 , 0.40000001, 0.33333334, 0.2857143 , 0.25 , 0.22222222, 0.2 , 0.18181819, 0.16666667, 0.15384616, 0.14285715, 0.13333334, 0.125 , 0.11764706, 0.11111111, 0.10526316, 0.1 ], [ 3. , 1.5 , 1. , 0.75 , 0.60000002, 0.5 , 0.42857143, 0.375 , 0.33333334, 0.30000001, 0.27272728, 0.25 , 0.23076923, 0.21428572, 0.2 , 0.1875 , 0.17647059, 0.16666667, 0.15789473, 0.15000001], [ 4. , 2. , 1.33333337, 1. , 0.80000001, 0.66666669, 0.5714286 , 0.5 , 0.44444445, 0.40000001, 0.36363637, 0.33333334, 0.30769232, 0.2857143 , 0.26666668, 0.25 , 0.23529412, 0.22222222, 0.21052632, 0.2 ], [ 5. , 2.5 , 1.66666663, 1.25 , 1. , 0.83333331, 0.71428573, 0.625 , 0.55555558, 0.5 , 0.45454547, 0.41666666, 0.38461539, 0.35714287, 0.33333334, 0.3125 , 0.29411766, 0.27777779, 0.2631579 , 0.25 ], [ 6. , 3. , 2. , 1.5 , 1.20000005, 1. , 0.85714287, 0.75 , 0.66666669, 0.60000002, 0.54545456, 0.5 , 0.46153846, 0.42857143, 0.40000001, 0.375 , 0.35294119, 0.33333334, 0.31578946, 0.30000001], [ 7. , 3.5 , 2.33333325, 1.75 , 1.39999998, 1.16666663, 1. , 0.875 , 0.77777779, 0.69999999, 0.63636363, 0.58333331, 0.53846157, 0.5 , 0.46666667, 0.4375 , 0.41176471, 0.3888889 , 0.36842105, 0.34999999], [ 8. , 4. , 2.66666675, 2. , 1.60000002, 1.33333337, 1.14285719, 1. , 0.8888889 , 0.80000001, 0.72727275, 0.66666669, 0.61538464, 0.5714286 , 0.53333336, 0.5 , 0.47058824, 0.44444445, 0.42105263, 0.40000001], [ 9. , 4.5 , 3. , 2.25 , 1.79999995, 1.5 , 1.28571427, 1.125 , 1. , 0.89999998, 0.81818181, 0.75 , 0.69230771, 0.64285713, 0.60000002, 0.5625 , 0.52941179, 0.5 , 0.47368422, 0.44999999], [ 10. , 5. , 3.33333325, 2.5 , 2. , 1.66666663, 1.42857146, 1.25 , 1.11111116, 1. , 0.90909094, 0.83333331, 0.76923078, 0.71428573, 0.66666669, 0.625 , 0.58823532, 0.55555558, 0.52631581, 0.5 ]]) >>> p array([[ 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21.], [ 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22.], [ 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23.], [ 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24.], [ 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.], [ 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26.], [ 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27.], [ 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28.], [ 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29.], [ 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30.]]) Note that you have to provide the shape information for ux and uy because fortran writes them together as a stream of 400 numbers. -Neil

10 10

Add guaranteed no-copy to array creation and reshape?
by Sebastian Berg 13 Jan '19

13 Jan '19

Hi all, In https://github.com/numpy/numpy/pull/11897 I am looking into the addition of a `copy=np.never_copy` argument to: * np.array * arr.reshape/np.reshape * arr.astype Which would cause an error to be raised when numpy cannot guarantee that the returned array is a view of the input array. The motivation is to easier avoid accidental copies of large data, or ensure that in-place manipulation will be meaningful. The copy flag API would be: * `copy=True` forces a copy * `copy=False` allows numpy to copy if necessary * `copy=np.never_copy` will error if a copy would be necessary * (almost) all other input will be deprecated. Unfortunately using `copy="never"` is tricky, because currently `np.array(..., copy="never")` behaves exactly the same as `np.array(..., copy=bool("never"))`. So that the wrong result would be given on old numpy versions and it would be a behaviour change. Some things that are a not so nice maybe: * adding/using `np.never_copy` is not very nice * Scalars need a copy and so will not be allowed * For rare array-likes numpy may not be able to guarantee no-copy, although it could happen (but should not). The history is that a long while ago I considered adding a copy flag to `reshape` so that it is possible to do `copy=np.never_copy` (or similar) to ensure that no copy is made. In these, you may want something like an assertion: ``` new_arr = arr.reshape(new_shape) assert np.may_share_memory(arr, new_arr) # Which is sometimes -- but should not be -- written as: arr.shape = new_shape # unnecessary container modification # Or: view = np.array(arr, order="F") assert np.may_share_memory(arr, new_arr) ``` but is more readable and will not cause an intermediate copy on error. So what do you think? Other variants would be to not expose this for `np.array` and probably limit `copy="never"` to the reshape method. Or just to not do it at all. Or to also accept "never" for `reshape`, although I think I would prefer to keep it in sync and wait for a few years to consider that. Best, Sebastian

9 32

three-dim array
by Jeff 27 Dec '18

27 Dec '18

hello, sorry newbe to numpy. I want to define a three-dim array. I know this works: >>> np.array([[[1,2],[3,4]],[[5,6],[7,8]]]) array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]) But can you tell why this doesnt work? >>> np.array([[1,2],[[1,2],[3,4]]]) Traceback (most recent call last): File "<stdin>", line 1, in <module> ValueError: setting an array element with a sequence. Thank you.

6 6

ANN: SfePy 2018.4
by Robert Cimrman 27 Dec '18

27 Dec '18

I am pleased to announce release 2018.4 of SfePy. Description ----------- SfePy (simple finite elements in Python) is a software for solving systems of coupled partial differential equations by the finite element method or by the isogeometric analysis (limited support). It is distributed under the new BSD license. Home page: http://sfepy.org Mailing list: https://mail.python.org/mm3/mailman3/lists/sfepy.python.org/ Git (source) repository, issue tracker: https://github.com/sfepy/sfepy Highlights of this release -------------------------- - better support for eigenvalue problems - improved MUMPS solver interface - support for logging and plotting of complex values For full release notes see [1]. Cheers, Robert Cimrman [1] http://docs.sfepy.org/doc/release_notes.html#id1 --- Contributors to this release in alphabetical order: Robert Cimrman Vladimir Lukes Matyas Novak Jan Heczko Lubos Kejzlar

1 0

Warn or immidiately change readonly flag on broadcast_arrays return value?
by Matti Picus 25 Dec '18

25 Dec '18

In PR 12609 https://github.com/numpy/numpy/pull/12609 I added code to emit a DepricationWarning when broadcast_arrays returns an array where the output is repeated. While this is a minimal fix to the problem, perhaps we should consider making the output readonly immediately instead? - A deprecation cycle requires two changes to downstream user's code: one to filter the deprecation warning, and another when we actually make the change - Writing to the repeated data will cause errors now. What do you think, should we change the behaviour at all, and if so should we depricate it over two releases or change it immediately? The original issue is here https://github.com/numpy/numpy/issues/2705 Matti

4 3

ANN: NumExpr 2.6.9
by Robert McLeod 21 Dec '18

21 Dec '18

Hi everyone, This is a version-bump release to provide wheels for Python 3.7.1 on Windows platforms. Also Mike Toews made the handling of our environment variables more robust. Project documentation is available at: http://numexpr.readthedocs.io/ Changes from 2.6.8 to 2.6.9 --------------------------- - Thanks to Mike Toews for more robust handling of the thread-setting environment variables. - With Appveyor updating to Python 3.7.1, wheels for Python 3.7 are now available in addition to those for other OSes. What's Numexpr? --------------- Numexpr is a fast numerical expression evaluator for NumPy. With it, expressions that operate on arrays (like "3*a+4*b") are accelerated and use less memory than doing the same calculation in Python. It has multi-threaded capabilities, as well as support for Intel's MKL (Math Kernel Library), which allows an extremely fast evaluation of transcendental functions (sin, cos, tan, exp, log...) while squeezing the last drop of performance out of your multi-core processors. Look here for a some benchmarks of numexpr using MKL: https://github.com/pydata/numexpr/wiki/NumexprMKL Its only dependency is NumPy (MKL is optional), so it works well as an easy-to-deploy, easy-to-use, computational engine for projects that don't want to adopt other solutions requiring more heavy dependencies. Where I can find Numexpr? ------------------------- The project is hosted at GitHub in: https://github.com/pydata/numexpr You can get the packages from PyPI as well (but not for RC releases): http://pypi.python.org/pypi/numexpr Documentation is hosted at: http://numexpr.readthedocs.io/en/latest/ Share your experience --------------------- Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. Enjoy data! -- Robert McLeod, Ph.D. robbmcleod(a)gmail.com robbmcleod(a)protonmail.com robert.mcleod(a)hitachi-hhtc.ca www.entropyreduction.al

1 0

NumPy 1.16.0rc1 released
by Charles R Harris 20 Dec '18

20 Dec '18

Hi All, On behalf of the NumPy team I'm pleased to announce the release of NumPy 1.16.0rc1. This is the last NumPy release to support Python 2.7 and will be maintained as a long term release with bug fixes until 2020. This release has seen a lot of refactoring and features many bug fixes, improved code organization, and better cross platform compatibility. Not all of these improvements will be visible to users, but they should help make maintenance easier going forward. Highlights are - Experimental support for overriding numpy functions in downstream projects. - The matmul function is now a ufunc and can be overridden using __array_ufunc__. - Improved support for the ARM and POWER architectures. - Improved support for AIX and PyPy. - Improved interoperation with ctypes. - Improved support for PEP 3118. The supported Python versions are 2.7 and 3.5-3.7, support for 3.4 has been dropped. The wheels on PyPI are linked with OpenBLAS v0.3.4+, which should fix the known threading issues found in previous OpenBLAS versions. Downstream developers building this release should use Cython >= 0.29 and, if linking OpenBLAS, OpenBLAS > v0.3.4. Wheels for this release can be downloaded from PyPI <https://pypi.org/project/numpy/1.16.0rc1/>, source archives are available from Github <https://github.com/numpy/numpy/releases/tag/v1.16.0rc1>. *Contributors* A total of 111 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Alan Fontenot + - Allan Haldane - Alon Hershenhorn + - Alyssa Quek + - Andreas Nussbaumer + - Anner + - Anthony Sottile + - Antony Lee - Ayappan P + - Bas van Schaik + - C.A.M. Gerlach + - Charles Harris - Chris Billington - Christian Clauss - Christoph Gohlke - Christopher Pezley + - Daniel B Allan + - Daniel Smith - Dawid Zych + - Derek Kim + - Dima Pasechnik + - Edgar Giovanni Lepe + - Elena Mokeeva + - Elliott Sales de Andrade + - Emil Hessman + - Eric Schles + - Eric Wieser - Giulio Benetti + - Guillaume Gautier + - Guo Ci - Heath Henley + - Isuru Fernando + - J. Lewis Muir + - Jack Vreeken + - Jaime Fernandez - James Bourbeau - Jeff VanOss - Jeffrey Yancey + - Jeremy Chen + - Jeremy Manning + - Jeroen Demeyer - John Darbyshire + - John Zwinck - Jonas Jensen + - Joscha Reimer + - Juan Azcarreta + - Julian Taylor - Kevin Sheppard - Krzysztof Chomski + - Kyle Sunden - Lars Grüter - Lilian Besson + - MSeifert04 - Mark Harfouche - Marten van Kerkwijk - Martin Thoma - Matt Harrigan + - Matthew Bowden + - Matthew Brett - Matthias Bussonnier - Matti Picus - Max Aifer + - Michael Hirsch, Ph.D + - Michael James Jamie Schnaitter + - MichaelSaah + - Mike Toews - Minkyu Lee + - Mircea Akos Bruma + - Mircea-Akos Brumă + - Moshe Looks + - Muhammad Kasim + - Nathaniel J. Smith - Nikita Titov + - Paul Müller + - Paul van Mulbregt - Pauli Virtanen - Pierre Glaser + - Pim de Haan - Ralf Gommers - Robert Kern - Robin Aggleton + - Rohit Pandey + - Roman Yurchak + - Ryan Soklaski - Sebastian Berg - Sho Nakamura + - Simon Gibbons - Stan Seibert + - Stefan Otte - Stefan van der Walt - Stephan Hoyer - Stuart Archibald - Taylor Smith + - Tim Felgentreff + - Tim Swast + - Tim Teichmann + - Toshiki Kataoka - Travis Oliphant - Tyler Reddy - Uddeshya Singh + - Warren Weckesser - Weitang Li + - Wenjamin Petrenko + - William D. Irons - Yannick Jadoul + - Yaroslav Halchenko - Yug Khanna + - Yuji Kanagawa + - Yukun Guo + - lerbuke + - @ankokumoyashi + Cheers, Charles Harris

1 0

Adding "maximum difference" to np.testing.assert_array_equal errors
by Stephan Hoyer 20 Dec '18

20 Dec '18

I have a PR up for review that adds "maximum difference" to the error messages produced by NumPy's testing functions for comparing arrays: https://github.com/numpy/numpy/pull/12591 Because this changes NumPy's public interface, I'm running it by the mailing list to see if there are any objections or suggestions for improvement. Example behavior: >>> x = np.array([1, 2, 3]) >>> y = np.array([1, 2, 3.0001]) >>> np.testing.assert_allclose(x, y) AssertionError: Not equal to tolerance rtol=1e-07, atol=0 (mismatch 33.333333333333336%, maximum difference 0.00010000000000021103) x: array([1, 2, 3]) y: array([1. , 2. , 3.0001]) Motivation: when writing numerical algorithms, I frequently find myself experimenting to pick the right value of atol and rtol for np.testing.assert_allclose(). If I make the tolerance too generous, I risk missing regressions in accuracy, so I usually try to pick the smallest values for which tests pass. This change immediately reveals appropriate values to use for these parameters, so I don't need to guess and check. One alternative would be print both "atol" and "rtol" numbers directly that could be immediately used in assert_allclose(). Here we effectively only have "atol". The main reason why I didn't do this is that I felt it would add more clutter (rtol is slightly redundant with the displayed array values). But we could probably do this if we're willing to split the error message onto a few more lines, e.g., AssertionError: Not equal to tolerance rtol=1e-07, atol=0 (mismatch 33.333333333333336%, maximum absolute difference 0.00010000000000021103, maximum relative difference 3.333333333340368e-05) x: array([1, 2, 3]) y: array([1. , 2. , 3.0001]) Best, Stephan

4 4

Reminder: weekly status meeting Dec 19 at 12:00 pacific time
by Matti Picus 19 Dec '18

19 Dec '18

The draft agenda is at https://hackmd.io/Gn1ymjwkRjm9WVY5Cgbwsw?both This will be our last meeting of 2018. The BIDS team

1 0

ANN: SciPy 1.2.0
by Tyler Reddy 18 Dec '18

18 Dec '18

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA256 Hi all, On behalf of the SciPy development team I'm pleased to announce the release of SciPy 1.2.0. This is an LTS release and the last to support Python 2.7. Sources and binary wheels can be found at: https://pypi.org/project/scipy/ and at: https://github.com/scipy/scipy/releases/tag/v1.2.0 One of a few ways to install this release with pip: pip install scipy==1.2.0 ========================== SciPy 1.2.0 Release Notes ========================== SciPy 1.2.0 is the culmination of 6 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. 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). Our development attention will now shift to bug-fix releases on the 1.2.x branch, and on adding new features on the master branch. This release requires Python 2.7 or 3.4+ and NumPy 1.8.2 or greater. Note: This will be the last SciPy release to support Python 2.7. Consequently, the 1.2.x series will be a long term support (LTS) release; we will backport bug fixes until 1 Jan 2020. For running on PyPy, PyPy3 6.0+ and NumPy 1.15.0 are required. Highlights of this release --------------------------- - 1-D root finding improvements with a new solver, ``toms748``, and a new unified interface, ``root_scalar`` - New ``dual_annealing`` optimization method that combines stochastic and local deterministic searching - A new optimization algorithm, ``shgo`` (simplicial homology global optimization) for derivative free optimization problems - A new category of quaternion-based transformations are available in `scipy.spatial.transform` New features ============ `scipy.ndimage` improvements --------------------------------- Proper spline coefficient calculations have been added for the ``mirror``, ``wrap``, and ``reflect`` modes of `scipy.ndimage.rotate` `scipy.fftpack` improvements --------------------------------- DCT-IV, DST-IV, DCT-I, and DST-I orthonormalization are now supported in `scipy.fftpack`. `scipy.interpolate` improvements --------------------------------- `scipy.interpolate.pade` now accepts a new argument for the order of the numerator `scipy.cluster` improvements ----------------------------- `scipy.cluster.vq.kmeans2` gained a new initialization method, kmeans++. `scipy.special` improvements ----------------------------- The function ``softmax`` was added to `scipy.special`. `scipy.optimize` improvements ------------------------------ The one-dimensional nonlinear solvers have been given a unified interface `scipy.optimize.root_scalar`, similar to the `scipy.optimize.root` interface for multi-dimensional solvers. ``scipy.optimize.root_scalar(f, bracket=[a ,b], method="brenth")`` is equivalent to ``scipy.optimize.brenth(f, a ,b)``. If no ``method`` is specified, an appropriate one will be selected based upon the bracket and the number of derivatives available. The so-called Algorithm 748 of Alefeld, Potra and Shi for root-finding within an enclosing interval has been added as `scipy.optimize.toms748`. This provides guaranteed convergence to a root with convergence rate per function evaluation of approximately 1.65 (for sufficiently well-behaved functions.) ``differential_evolution`` now has the ``updating`` and ``workers`` keywords. The first chooses between continuous updating of the best solution vector (the default), or once per generation. Continuous updating can lead to faster convergence. The ``workers`` keyword accepts an ``int`` or map-like callable, and parallelises the solver (having the side effect of updating once per generation). Supplying an ``int`` evaluates the trial solutions in N parallel parts. Supplying a map-like callable allows other parallelisation approaches (such as ``mpi4py``, or ``joblib``) to be used. ``dual_annealing`` (and ``shgo`` below) is a powerful new general purpose global optizimation (GO) algorithm. ``dual_annealing`` uses two annealing processes to accelerate the convergence towards the global minimum of an objective mathematical function. The first annealing process controls the stochastic Markov chain searching and the second annealing process controls the deterministic minimization. So, dual annealing is a hybrid method that takes advantage of stochastic and local deterministic searching in an efficient way. ``shgo`` (simplicial homology global optimization) is a similar algorithm appropriate for solving black box and derivative free optimization (DFO) problems. The algorithm generally converges to the global solution in finite time. The convergence holds for non-linear inequality and equality constraints. In addition to returning a global minimum, the algorithm also returns any other global and local minima found after every iteration. This makes it useful for exploring the solutions in a domain. `scipy.optimize.newton` can now accept a scalar or an array ``MINPACK`` usage is now thread-safe, such that ``MINPACK`` + callbacks may be used on multiple threads. `scipy.signal` improvements ---------------------------- Digital filter design functions now include a parameter to specify the sampling rate. Previously, digital filters could only be specified using normalized frequency, but different functions used different scales (e.g. 0 to 1 for ``butter`` vs 0 to π for ``freqz``), leading to errors and confusion. With the ``fs`` parameter, ordinary frequencies can now be entered directly into functions, with the normalization handled internally. ``find_peaks`` and related functions no longer raise an exception if the properties of a peak have unexpected values (e.g. a prominence of 0). A ``PeakPropertyWarning`` is given instead. The new keyword argument ``plateau_size`` was added to ``find_peaks``. ``plateau_size`` may be used to select peaks based on the length of the flat top of a peak. ``welch()`` and ``csd()`` methods in `scipy.signal` now support calculation of a median average PSD, using ``average='mean'`` keyword `scipy.sparse` improvements ---------------------------- The `scipy.sparse.bsr_matrix.tocsr` method is now implemented directly instead of converting via COO format, and the `scipy.sparse.bsr_matrix.tocsc` method is now also routed via CSR conversion instead of COO. The efficiency of both conversions is now improved. The issue where SuperLU or UMFPACK solvers crashed on matrices with non-canonical format in `scipy.sparse.linalg` was fixed. The solver wrapper canonicalizes the matrix if necessary before calling the SuperLU or UMFPACK solver. The ``largest`` option of `scipy.sparse.linalg.lobpcg()` was fixed to have a correct (and expected) behavior. The order of the eigenvalues was made consistent with the ARPACK solver (``eigs()``), i.e. ascending for the smallest eigenvalues, and descending for the largest eigenvalues. The `scipy.sparse.random` function is now faster and also supports integer and complex values by passing the appropriate value to the ``dtype`` argument. `scipy.spatial` improvements ----------------------------- The function `scipy.spatial.distance.jaccard` was modified to return 0 instead of ``np.nan`` when two all-zero vectors are compared. Support for the Jensen Shannon distance, the square-root of the divergence, has been added under `scipy.spatial.distance.jensenshannon` An optional keyword was added to the function `scipy.spatial.cKDTree.query_ball_point()` to sort or not sort the returned indices. Not sorting the indices can speed up calls. A new category of quaternion-based transformations are available in `scipy.spatial.transform`, including spherical linear interpolation of rotations (``Slerp``), conversions to and from quaternions, Euler angles, and general rotation and inversion capabilities (`spatial.transform.Rotation`), and uniform random sampling of 3D rotations (`spatial.transform.Rotation.random`). `scipy.stats` improvements --------------------------- The Yeo-Johnson power transformation is now supported (``yeojohnson``, ``yeojohnson_llf``, ``yeojohnson_normmax``, ``yeojohnson_normplot``). Unlike the Box-Cox transformation, the Yeo-Johnson transformation can accept negative values. Added a general method to sample random variates based on the density only, in the new function ``rvs_ratio_uniforms``. The Yule-Simon distribution (``yulesimon``) was added -- this is a new discrete probability distribution. ``stats`` and ``mstats`` now have access to a new regression method, ``siegelslopes``, a robust linear regression algorithm `scipy.stats.gaussian_kde` now has the ability to deal with weighted samples, and should have a modest improvement in performance Levy Stable Parameter Estimation, PDF, and CDF calculations are now supported for `scipy.stats.levy_stable`. The Brunner-Munzel test is now available as ``brunnermunzel`` in ``stats`` and ``mstats`` `scipy.linalg` improvements --------------------------- `scipy.linalg.lapack` now exposes the LAPACK routines using the Rectangular Full Packed storage (RFP) for upper triangular, lower triangular, symmetric, or Hermitian matrices; the upper trapezoidal fat matrix RZ decomposition routines are now available as well. Deprecated features =================== The functions ``hyp2f0``, ``hyp1f2`` and ``hyp3f0`` in ``scipy.special`` have been deprecated. Backwards incompatible changes ============================== LAPACK version 3.4.0 or later is now required. Building with Apple Accelerate is no longer supported. The function ``scipy.linalg.subspace_angles(A, B)`` now gives correct results for all angles. Before this, the function only returned correct values for those angles which were greater than pi/4. Support for the Bento build system has been removed. Bento has not been maintained for several years, and did not have good Python 3 or wheel support, hence it was time to remove it. The required signature of `scipy.optimize.lingprog` ``method=simplex`` callback function has changed. Before iteration begins, the simplex solver first converts the problem into a standard form that does not, in general, have the same variables or constraints as the problem defined by the user. Previously, the simplex solver would pass a user-specified callback function several separate arguments, such as the current solution vector ``xk``, corresponding to this standard form problem. Unfortunately, the relationship between the standard form problem and the user-defined problem was not documented, limiting the utility of the information passed to the callback function. In addition to numerous bug fix changes, the simplex solver now passes a user-specified callback function a single ``OptimizeResult`` object containing information that corresponds directly to the user-defined problem. In future releases, this ``OptimizeResult`` object may be expanded to include additional information, such as variables corresponding to the standard-form problem and information concerning the relationship between the standard-form and user-defined problems. The implementation of `scipy.sparse.random` has changed, and this affects the numerical values returned for both ``sparse.random`` and ``sparse.rand`` for some matrix shapes and a given seed. `scipy.optimize.newton` will no longer use Halley's method in cases where it negatively impacts convergence Other changes ============= Authors ======= * @endolith * @luzpaz * Hameer Abbasi + * akahard2dj + * Anton Akhmerov * Joseph Albert * alexthomas93 + * ashish + * atpage + * Blair Azzopardi + * Yoshiki Vázquez Baeza * Bence Bagi + * Christoph Baumgarten * Lucas Bellomo + * BH4 + * Aditya Bharti * Max Bolingbroke * François Boulogne * Ward Bradt + * Matthew Brett * Evgeni Burovski * Rafał Byczek + * Alfredo Canziani + * CJ Carey * Lucía Cheung + * Poom Chiarawongse + * Jeanne Choo + * Robert Cimrman * Graham Clenaghan + * cynthia-rempel + * Johannes Damp + * Jaime Fernandez del Rio * Dowon + * emmi474 + * Stefan Endres + * Thomas Etherington + * Piotr Figiel * Alex Fikl + * fo40225 + * Joseph Fox-Rabinovitz * Lars G * Abhinav Gautam + * Stiaan Gerber + * C.A.M. Gerlach + * Ralf Gommers * Todd Goodall * Lars Grueter + * Sylvain Gubian + * Matt Haberland * David Hagen * Will Handley + * Charles Harris * Ian Henriksen * Thomas Hisch + * Theodore Hu * Michael Hudson-Doyle + * Nicolas Hug + * jakirkham + * Jakob Jakobson + * James + * Jan Schlüter * jeanpauphilet + * josephmernst + * Kai + * Kai-Striega + * kalash04 + * Toshiki Kataoka + * Konrad0 + * Tom Krauss + * Johannes Kulick * Lars Grüter + * Eric Larson * Denis Laxalde * Will Lee + * Katrin Leinweber + * Yin Li + * P. L. Lim + * Jesse Livezey + * Duncan Macleod + * MatthewFlamm + * Nikolay Mayorov * Mike McClurg + * Christian Meyer + * Mark Mikofski * Naoto Mizuno + * mohmmadd + * Nathan Musoke * Anju Geetha Nair + * Andrew Nelson * Ayappan P + * Nick Papior * Haesun Park + * Ronny Pfannschmidt + * pijyoi + * Ilhan Polat * Anthony Polloreno + * Ted Pudlik * puenka * Eric Quintero * Pradeep Reddy Raamana + * Vyas Ramasubramani + * Ramon Viñas + * Tyler Reddy * Joscha Reimer * Antonio H Ribeiro * richardjgowers + * Rob + * robbystk + * Lucas Roberts + * rohan + * Joaquin Derrac Rus + * Josua Sassen + * Bruce Sharpe + * Max Shinn + * Scott Sievert * Sourav Singh * Strahinja Lukić + * Kai Striega + * Shinya SUZUKI + * Mike Toews + * Piotr Uchwat * Miguel de Val-Borro + * Nicky van Foreest * Paul van Mulbregt * Gael Varoquaux * Pauli Virtanen * Stefan van der Walt * Warren Weckesser * Joshua Wharton + * Bernhard M. Wiedemann + * Eric Wieser * Josh Wilson * Tony Xiang + * Roman Yurchak + * Roy Zywina + A total of 137 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 1.2.0 ------------------------ * `#9520 <https://github.com/scipy/scipy/issues/9520>`__: signal.correlate with method='fft' doesn't benefit from long... * `#9547 <https://github.com/scipy/scipy/issues/9547>`__: signature of dual_annealing doesn't match other optimizers * `#9540 <https://github.com/scipy/scipy/issues/9540>`__: SciPy v1.2.0rc1 cannot be imported on Python 2.7.15 * `#1240 <https://github.com/scipy/scipy/issues/1240>`__: Allowing multithreaded use of minpack through scipy.optimize... * `#1432 <https://github.com/scipy/scipy/issues/1432>`__: scipy.stats.mode extremely slow (Trac #905) * `#3372 <https://github.com/scipy/scipy/issues/3372>`__: Please add Sphinx search field to online scipy html docs * `#3678 <https://github.com/scipy/scipy/issues/3678>`__: _clough_tocher_2d_single direction between centroids * `#4174 <https://github.com/scipy/scipy/issues/4174>`__: lobpcg "largest" option invalid? * `#5493 <https://github.com/scipy/scipy/issues/5493>`__: anderson_ksamp p-values>1 * `#5743 <https://github.com/scipy/scipy/issues/5743>`__: slsqp fails to detect infeasible problem * `#6139 <https://github.com/scipy/scipy/issues/6139>`__: scipy.optimize.linprog failed to find a feasible starting point... * `#6358 <https://github.com/scipy/scipy/issues/6358>`__: stats: docstring for `vonmises_line` points to `vonmises_line`... * `#6498 <https://github.com/scipy/scipy/issues/6498>`__: runtests.py is missing in pypi distfile * `#7426 <https://github.com/scipy/scipy/issues/7426>`__: scipy.stats.ksone(n).pdf(x) returns nan for positive values of... * `#7455 <https://github.com/scipy/scipy/issues/7455>`__: scipy.stats.ksone.pdf(2,x) return incorrect values for x near... * `#7456 <https://github.com/scipy/scipy/issues/7456>`__: scipy.special.smirnov and scipy.special.smirnovi have accuracy... * `#7492 <https://github.com/scipy/scipy/issues/7492>`__: scipy.special.kolmogorov(x)/kolmogi(p) inefficient, inaccurate... * `#7914 <https://github.com/scipy/scipy/issues/7914>`__: TravisCI not failing when it should for -OO run * `#8064 <https://github.com/scipy/scipy/issues/8064>`__: linalg.solve test crashes on Windows * `#8212 <https://github.com/scipy/scipy/issues/8212>`__: LAPACK Rectangular Full Packed routines * `#8256 <https://github.com/scipy/scipy/issues/8256>`__: differential_evolution bug converges to wrong results in complex... * `#8443 <https://github.com/scipy/scipy/issues/8443>`__: Deprecate `hyp2f0`, `hyp1f2`, and `hyp3f0`? * `#8452 <https://github.com/scipy/scipy/issues/8452>`__: DOC: ARPACK tutorial has two conflicting equations * `#8680 <https://github.com/scipy/scipy/issues/8680>`__: scipy fails compilation when building from source * `#8686 <https://github.com/scipy/scipy/issues/8686>`__: Division by zero in _trustregion.py when x0 is exactly equal... * `#8700 <https://github.com/scipy/scipy/issues/8700>`__: _MINPACK_LOCK not held when calling into minpack from least_squares * `#8786 <https://github.com/scipy/scipy/issues/8786>`__: erroneous moment values for t-distribution * `#8791 <https://github.com/scipy/scipy/issues/8791>`__: Checking COLA condition in istft should be optional (or omitted) * `#8843 <https://github.com/scipy/scipy/issues/8843>`__: imresize cannot be deprecated just yet * `#8844 <https://github.com/scipy/scipy/issues/8844>`__: Inverse Wishart Log PDF Incorrect for Non-diagonal Scale Matrix? * `#8878 <https://github.com/scipy/scipy/issues/8878>`__: vonmises and vonmises_line in stats: vonmises wrong and superfluous? * `#8895 <https://github.com/scipy/scipy/issues/8895>`__: v1.1.0 `ndi.rotate` documentation – reused parameters not filled... * `#8900 <https://github.com/scipy/scipy/issues/8900>`__: Missing complex conjugation in scipy.sparse.linalg.LinearOperator * `#8904 <https://github.com/scipy/scipy/issues/8904>`__: BUG: if zero derivative at root, then Newton fails with RuntimeWarning * `#8911 <https://github.com/scipy/scipy/issues/8911>`__: make_interp_spline bc_type incorrect input interpretation * `#8942 <https://github.com/scipy/scipy/issues/8942>`__: MAINT: Refactor `_linprog.py` and `_linprog_ip.py` to remove... * `#8947 <https://github.com/scipy/scipy/issues/8947>`__: np.int64 in scipy.fftpack.next_fast_len * `#9020 <https://github.com/scipy/scipy/issues/9020>`__: BUG: linalg.subspace_angles gives wrong results * `#9033 <https://github.com/scipy/scipy/issues/9033>`__: scipy.stats.normaltest sometimes gives incorrect returns b/c... * `#9036 <https://github.com/scipy/scipy/issues/9036>`__: Bizarre times for `scipy.sparse.rand` function with 'low' density... * `#9044 <https://github.com/scipy/scipy/issues/9044>`__: optimize.minimize(method=`trust-constr`) result dict does not... * `#9071 <https://github.com/scipy/scipy/issues/9071>`__: doc/linalg: add cho_solve_banded to see also of cholesky_banded * `#9082 <https://github.com/scipy/scipy/issues/9082>`__: eigenvalue sorting in scipy.sparse.linalg.eigsh * `#9086 <https://github.com/scipy/scipy/issues/9086>`__: signaltools.py:491: FutureWarning: Using a non-tuple sequence... * `#9091 <https://github.com/scipy/scipy/issues/9091>`__: test_spline_filter failure on 32-bit * `#9122 <https://github.com/scipy/scipy/issues/9122>`__: Typo on scipy minimization tutorial * `#9135 <https://github.com/scipy/scipy/issues/9135>`__: doc error at https://docs.scipy.org/doc/scipy/reference/tutorial/stats/discrete_poisson.… * `#9167 <https://github.com/scipy/scipy/issues/9167>`__: DOC: BUG: typo in ndimage LowLevelCallable tutorial example * `#9169 <https://github.com/scipy/scipy/issues/9169>`__: truncnorm does not work if b < a in scipy.stats * `#9250 <https://github.com/scipy/scipy/issues/9250>`__: scipy.special.tests.test_mpmath::TestSystematic::test_pcfw fails... * `#9259 <https://github.com/scipy/scipy/issues/9259>`__: rv.expect() == rv.mean() is false for rv.mean() == nan (and inf) * `#9286 <https://github.com/scipy/scipy/issues/9286>`__: DOC: Rosenbrock expression in optimize.minimize tutorial * `#9316 <https://github.com/scipy/scipy/issues/9316>`__: SLSQP fails in nested optimization * `#9337 <https://github.com/scipy/scipy/issues/9337>`__: scipy.signal.find_peaks key typo in documentation * `#9345 <https://github.com/scipy/scipy/issues/9345>`__: Example from documentation of scipy.sparse.linalg.eigs raises... * `#9383 <https://github.com/scipy/scipy/issues/9383>`__: Default value for "mode" in "ndimage.shift" * `#9419 <https://github.com/scipy/scipy/issues/9419>`__: dual_annealing off by one in the number of iterations * `#9442 <https://github.com/scipy/scipy/issues/9442>`__: Error in Defintion of Rosenbrock Function * `#9453 <https://github.com/scipy/scipy/issues/9453>`__: TST: test_eigs_consistency() doesn't have consistent results Pull requests for 1.2.0 ------------------------ * `#9526 <https://github.com/scipy/scipy/pull/9526>`__: TST: relax precision requirements in signal.correlate tests * `#9507 <https://github.com/scipy/scipy/pull/9507>`__: CI: MAINT: Skip a ckdtree test on pypy * `#9512 <https://github.com/scipy/scipy/pull/9512>`__: TST: test_random_sampling 32-bit handling * `#9494 <https://github.com/scipy/scipy/pull/9494>`__: TST: test_kolmogorov xfail 32-bit * `#9486 <https://github.com/scipy/scipy/pull/9486>`__: BUG: fix sparse random int handling * `#9550 <https://github.com/scipy/scipy/pull/9550>`__: BUG: scipy/_lib/_numpy_compat: get_randint * `#9549 <https://github.com/scipy/scipy/pull/9549>`__: MAINT: make dual_annealing signature match other optimizers * `#9541 <https://github.com/scipy/scipy/pull/9541>`__: BUG: fix SyntaxError due to non-ascii character on Python 2.7 * `#7352 <https://github.com/scipy/scipy/pull/7352>`__: ENH: add Brunner Munzel test to scipy.stats. * `#7373 <https://github.com/scipy/scipy/pull/7373>`__: BUG: Jaccard distance for all-zero arrays would return np.nan * `#7374 <https://github.com/scipy/scipy/pull/7374>`__: ENH: Add PDF, CDF and parameter estimation for Stable Distributions * `#8098 <https://github.com/scipy/scipy/pull/8098>`__: ENH: Add shgo for global optimization of NLPs. * `#8203 <https://github.com/scipy/scipy/pull/8203>`__: ENH: adding simulated dual annealing to optimize * `#8259 <https://github.com/scipy/scipy/pull/8259>`__: Option to follow original Storn and Price algorithm and its parallelisation * `#8293 <https://github.com/scipy/scipy/pull/8293>`__: ENH add ratio-of-uniforms method for rv generation to scipy.stats * `#8294 <https://github.com/scipy/scipy/pull/8294>`__: BUG: Fix slowness in stats.mode * `#8295 <https://github.com/scipy/scipy/pull/8295>`__: ENH: add Jensen Shannon distance to `scipy.spatial.distance` * `#8357 <https://github.com/scipy/scipy/pull/8357>`__: ENH: vectorize scalar zero-search-functions * `#8397 <https://github.com/scipy/scipy/pull/8397>`__: Add `fs=` parameter to filter design functions * `#8537 <https://github.com/scipy/scipy/pull/8537>`__: ENH: Implement mode parameter for spline filtering. * `#8558 <https://github.com/scipy/scipy/pull/8558>`__: ENH: small speedup for stats.gaussian_kde * `#8560 <https://github.com/scipy/scipy/pull/8560>`__: BUG: fix p-value calc of anderson_ksamp in scipy.stats * `#8614 <https://github.com/scipy/scipy/pull/8614>`__: ENH: correct p-values for stats.kendalltau and stats.mstats.kendalltau * `#8670 <https://github.com/scipy/scipy/pull/8670>`__: ENH: Require Lapack 3.4.0 * `#8683 <https://github.com/scipy/scipy/pull/8683>`__: Correcting kmeans documentation * `#8725 <https://github.com/scipy/scipy/pull/8725>`__: MAINT: Cleanup scipy.optimize.leastsq * `#8726 <https://github.com/scipy/scipy/pull/8726>`__: BUG: Fix _get_output in scipy.ndimage to support string * `#8733 <https://github.com/scipy/scipy/pull/8733>`__: MAINT: stats: A bit of clean up. * `#8737 <https://github.com/scipy/scipy/pull/8737>`__: BUG: Improve numerical precision/convergence failures of smirnov/kolmogorov * `#8738 <https://github.com/scipy/scipy/pull/8738>`__: MAINT: stats: A bit of clean up in test_distributions.py. * `#8740 <https://github.com/scipy/scipy/pull/8740>`__: BF/ENH: make minpack thread safe * `#8742 <https://github.com/scipy/scipy/pull/8742>`__: BUG: Fix division by zero in trust-region optimization methods * `#8746 <https://github.com/scipy/scipy/pull/8746>`__: MAINT: signal: Fix a docstring of a private function, and fix... * `#8750 <https://github.com/scipy/scipy/pull/8750>`__: DOC clarified description of norminvgauss in scipy.stats * `#8753 <https://github.com/scipy/scipy/pull/8753>`__: DOC: signal: Fix a plot title in the chirp docstring. * `#8755 <https://github.com/scipy/scipy/pull/8755>`__: DOC: MAINT: Fix link to the wheel documentation in developer... * `#8760 <https://github.com/scipy/scipy/pull/8760>`__: BUG: stats: boltzmann wasn't setting the upper bound. * `#8763 <https://github.com/scipy/scipy/pull/8763>`__: [DOC] Improved scipy.cluster.hierarchy documentation * `#8765 <https://github.com/scipy/scipy/pull/8765>`__: DOC: added example for scipy.stat.mstats.tmin * `#8788 <https://github.com/scipy/scipy/pull/8788>`__: DOC: fix definition of optional `disp` parameter * `#8802 <https://github.com/scipy/scipy/pull/8802>`__: MAINT: Suppress dd_real unused function compiler warnings. * `#8803 <https://github.com/scipy/scipy/pull/8803>`__: ENH: Add full_output support to optimize.newton() * `#8804 <https://github.com/scipy/scipy/pull/8804>`__: MAINT: stats cleanup * `#8808 <https://github.com/scipy/scipy/pull/8808>`__: DOC: add note about isinstance for frozen rvs * `#8812 <https://github.com/scipy/scipy/pull/8812>`__: Updated numpydoc submodule * `#8813 <https://github.com/scipy/scipy/pull/8813>`__: MAINT: stats: Fix multinomial docstrings, and do some clean up. * `#8816 <https://github.com/scipy/scipy/pull/8816>`__: BUG: fixed _stats of t-distribution in scipy.stats * `#8817 <https://github.com/scipy/scipy/pull/8817>`__: BUG: ndimage: Fix validation of the origin argument in correlate... * `#8822 <https://github.com/scipy/scipy/pull/8822>`__: BUG: integrate: Fix crash with repeated t values in odeint. * `#8832 <https://github.com/scipy/scipy/pull/8832>`__: Hyperlink DOIs against preferred resolver * `#8837 <https://github.com/scipy/scipy/pull/8837>`__: BUG: sparse: Ensure correct dtype for sparse comparison operations. * `#8839 <https://github.com/scipy/scipy/pull/8839>`__: DOC: stats: A few tweaks to the linregress docstring. * `#8846 <https://github.com/scipy/scipy/pull/8846>`__: BUG: stats: Fix logpdf method of invwishart. * `#8849 <https://github.com/scipy/scipy/pull/8849>`__: DOC: signal: Fixed mistake in the firwin docstring. * `#8854 <https://github.com/scipy/scipy/pull/8854>`__: DOC: fix type descriptors in ltisys documentation * `#8865 <https://github.com/scipy/scipy/pull/8865>`__: Fix tiny typo in docs for chi2 pdf * `#8870 <https://github.com/scipy/scipy/pull/8870>`__: Fixes related to invertibility of STFT * `#8872 <https://github.com/scipy/scipy/pull/8872>`__: ENH: special: Add the softmax function * `#8874 <https://github.com/scipy/scipy/pull/8874>`__: DOC correct gamma function in docstrings in scipy.stats * `#8876 <https://github.com/scipy/scipy/pull/8876>`__: ENH: Added TOMS Algorithm 748 as 1-d root finder; 17 test function... * `#8882 <https://github.com/scipy/scipy/pull/8882>`__: ENH: Only use Halley's adjustment to Newton if close enough. * `#8883 <https://github.com/scipy/scipy/pull/8883>`__: FIX: optimize: make jac and hess truly optional for 'trust-constr' * `#8885 <https://github.com/scipy/scipy/pull/8885>`__: TST: Do not error on warnings raised about non-tuple indexing. * `#8887 <https://github.com/scipy/scipy/pull/8887>`__: MAINT: filter out np.matrix PendingDeprecationWarning's in numpy... * `#8889 <https://github.com/scipy/scipy/pull/8889>`__: DOC: optimize: separate legacy interfaces from new ones * `#8890 <https://github.com/scipy/scipy/pull/8890>`__: ENH: Add optimize.root_scalar() as a universal dispatcher for... * `#8899 <https://github.com/scipy/scipy/pull/8899>`__: DCT-IV, DST-IV and DCT-I, DST-I orthonormalization support in... * `#8901 <https://github.com/scipy/scipy/pull/8901>`__: MAINT: Reorganize flapack.pyf.src file * `#8907 <https://github.com/scipy/scipy/pull/8907>`__: BUG: ENH: Check if guess for newton is already zero before checking... * `#8908 <https://github.com/scipy/scipy/pull/8908>`__: ENH: Make sorting optional for cKDTree.query_ball_point() * `#8910 <https://github.com/scipy/scipy/pull/8910>`__: DOC: sparse.csgraph simple examples. * `#8914 <https://github.com/scipy/scipy/pull/8914>`__: DOC: interpolate: fix equivalences of string aliases * `#8918 <https://github.com/scipy/scipy/pull/8918>`__: add float_control(precise, on) to _fpumode.c * `#8919 <https://github.com/scipy/scipy/pull/8919>`__: MAINT: interpolate: improve error messages for common `bc_type`... * `#8920 <https://github.com/scipy/scipy/pull/8920>`__: DOC: update Contributing to SciPy to say "prefer no PEP8 only... * `#8924 <https://github.com/scipy/scipy/pull/8924>`__: MAINT: special: deprecate `hyp2f0`, `hyp1f2`, and `hyp3f0` * `#8927 <https://github.com/scipy/scipy/pull/8927>`__: MAINT: special: remove `errprint` * `#8932 <https://github.com/scipy/scipy/pull/8932>`__: Fix broadcasting scale arg of entropy * `#8936 <https://github.com/scipy/scipy/pull/8936>`__: Fix (some) non-tuple index warnings * `#8937 <https://github.com/scipy/scipy/pull/8937>`__: ENH: implement sparse matrix BSR to CSR conversion directly. * `#8938 <https://github.com/scipy/scipy/pull/8938>`__: DOC: add @_ni_docstrings.docfiller in ndimage.rotate * `#8940 <https://github.com/scipy/scipy/pull/8940>`__: Update _discrete_distns.py * `#8943 <https://github.com/scipy/scipy/pull/8943>`__: DOC: Finish dangling sentence in `convolve` docstring * `#8944 <https://github.com/scipy/scipy/pull/8944>`__: MAINT: Address tuple indexing and warnings * `#8945 <https://github.com/scipy/scipy/pull/8945>`__: ENH: spatial.transform.Rotation [GSOC2018] * `#8950 <https://github.com/scipy/scipy/pull/8950>`__: csgraph Dijkstra function description rewording * `#8953 <https://github.com/scipy/scipy/pull/8953>`__: DOC, MAINT: HTTP -> HTTPS, and other linkrot fixes * `#8955 <https://github.com/scipy/scipy/pull/8955>`__: BUG: np.int64 in scipy.fftpack.next_fast_len * `#8958 <https://github.com/scipy/scipy/pull/8958>`__: MAINT: Add more descriptive error message for phase one simplex. * `#8962 <https://github.com/scipy/scipy/pull/8962>`__: BUG: sparse.linalg: add missing conjugate to _ScaledLinearOperator.adjoint * `#8963 <https://github.com/scipy/scipy/pull/8963>`__: BUG: sparse.linalg: downgrade LinearOperator TypeError to warning * `#8965 <https://github.com/scipy/scipy/pull/8965>`__: ENH: Wrapped RFP format and RZ decomposition routines * `#8969 <https://github.com/scipy/scipy/pull/8969>`__: MAINT: doc and code fixes for optimize.newton * `#8970 <https://github.com/scipy/scipy/pull/8970>`__: Added 'average' keyword for welch/csd to enable median averaging * `#8971 <https://github.com/scipy/scipy/pull/8971>`__: Better imresize deprecation warning * `#8972 <https://github.com/scipy/scipy/pull/8972>`__: MAINT: Switch np.where(c) for np.nonzero(c) * `#8975 <https://github.com/scipy/scipy/pull/8975>`__: MAINT: Fix warning-based failures * `#8979 <https://github.com/scipy/scipy/pull/8979>`__: DOC: fix description of count_sort keyword of dendrogram * `#8982 <https://github.com/scipy/scipy/pull/8982>`__: MAINT: optimize: Fixed minor mistakes in test_linprog.py (#8978) * `#8984 <https://github.com/scipy/scipy/pull/8984>`__: BUG: sparse.linalg: ensure expm casts integer inputs to float * `#8986 <https://github.com/scipy/scipy/pull/8986>`__: BUG: optimize/slsqp: do not exit with convergence on steps where... * `#8989 <https://github.com/scipy/scipy/pull/8989>`__: MAINT: use collections.abc in basinhopping * `#8990 <https://github.com/scipy/scipy/pull/8990>`__: ENH extend p-values of anderson_ksamp in scipy.stats * `#8991 <https://github.com/scipy/scipy/pull/8991>`__: ENH: Weighted kde * `#8993 <https://github.com/scipy/scipy/pull/8993>`__: ENH: spatial.transform.Rotation.random [GSOC 2018] * `#8994 <https://github.com/scipy/scipy/pull/8994>`__: ENH: spatial.transform.Slerp [GSOC 2018] * `#8995 <https://github.com/scipy/scipy/pull/8995>`__: TST: time.time in test * `#9007 <https://github.com/scipy/scipy/pull/9007>`__: Fix typo in fftpack.rst * `#9013 <https://github.com/scipy/scipy/pull/9013>`__: Added correct plotting code for two sided output from spectrogram * `#9014 <https://github.com/scipy/scipy/pull/9014>`__: BUG: differential_evolution with inf objective functions * `#9017 <https://github.com/scipy/scipy/pull/9017>`__: BUG: fixed #8446 corner case for asformat(array|dense) * `#9018 <https://github.com/scipy/scipy/pull/9018>`__: MAINT: _lib/ccallback: remove unused code * `#9021 <https://github.com/scipy/scipy/pull/9021>`__: BUG: Issue with subspace_angles * `#9022 <https://github.com/scipy/scipy/pull/9022>`__: DOC: Added "See Also" section to lombscargle docstring * `#9034 <https://github.com/scipy/scipy/pull/9034>`__: BUG: Fix tolerance printing behavior, remove meaningless tol... * `#9035 <https://github.com/scipy/scipy/pull/9035>`__: TST: improve signal.bsplines test coverage * `#9037 <https://github.com/scipy/scipy/pull/9037>`__: ENH: add a new init method for k-means * `#9039 <https://github.com/scipy/scipy/pull/9039>`__: DOC: Add examples to fftpack.irfft docstrings * `#9048 <https://github.com/scipy/scipy/pull/9048>`__: ENH: scipy.sparse.random * `#9050 <https://github.com/scipy/scipy/pull/9050>`__: BUG: scipy.io.hb_write: fails for matrices not in csc format * `#9051 <https://github.com/scipy/scipy/pull/9051>`__: MAINT: Fix slow sparse.rand for k < mn/3 (#9036). * `#9054 <https://github.com/scipy/scipy/pull/9054>`__: MAINT: spatial: Explicitly initialize LAPACK output parameters. * `#9055 <https://github.com/scipy/scipy/pull/9055>`__: DOC: Add examples to scipy.special docstrings * `#9056 <https://github.com/scipy/scipy/pull/9056>`__: ENH: Use one thread in OpenBLAS * `#9059 <https://github.com/scipy/scipy/pull/9059>`__: DOC: Update README with link to Code of Conduct * `#9060 <https://github.com/scipy/scipy/pull/9060>`__: BLD: remove support for the Bento build system. * `#9062 <https://github.com/scipy/scipy/pull/9062>`__: DOC add sections to overview in scipy.stats * `#9066 <https://github.com/scipy/scipy/pull/9066>`__: BUG: Correct "remez" error message * `#9069 <https://github.com/scipy/scipy/pull/9069>`__: DOC: update linalg section of roadmap for LAPACK versions. * `#9079 <https://github.com/scipy/scipy/pull/9079>`__: MAINT: add spatial.transform to refguide check; complete some... * `#9081 <https://github.com/scipy/scipy/pull/9081>`__: MAINT: Add warnings if pivot value is close to tolerance in linprog(method='simplex') * `#9084 <https://github.com/scipy/scipy/pull/9084>`__: BUG fix incorrect p-values of kurtosistest in scipy.stats * `#9095 <https://github.com/scipy/scipy/pull/9095>`__: DOC: add sections to mstats overview in scipy.stats * `#9096 <https://github.com/scipy/scipy/pull/9096>`__: BUG: Add test for Stackoverflow example from issue 8174. * `#9101 <https://github.com/scipy/scipy/pull/9101>`__: ENH: add Siegel slopes (robust regression) to scipy.stats * `#9105 <https://github.com/scipy/scipy/pull/9105>`__: allow resample_poly() to output float32 for float32 inputs. * `#9112 <https://github.com/scipy/scipy/pull/9112>`__: MAINT: optimize: make trust-constr accept constraint dict (#9043) * `#9118 <https://github.com/scipy/scipy/pull/9118>`__: Add doc entry to cholesky_banded * `#9120 <https://github.com/scipy/scipy/pull/9120>`__: eigsh documentation parameters * `#9125 <https://github.com/scipy/scipy/pull/9125>`__: interpolative: correctly reconstruct full rank matrices * `#9126 <https://github.com/scipy/scipy/pull/9126>`__: MAINT: Use warnings for unexpected peak properties * `#9129 <https://github.com/scipy/scipy/pull/9129>`__: BUG: Do not catch and silence KeyboardInterrupt * `#9131 <https://github.com/scipy/scipy/pull/9131>`__: DOC: Correct the typo in scipy.optimize tutorial page * `#9133 <https://github.com/scipy/scipy/pull/9133>`__: FIX: Avoid use of bare except * `#9134 <https://github.com/scipy/scipy/pull/9134>`__: DOC: Update of 'return_eigenvectors' description * `#9137 <https://github.com/scipy/scipy/pull/9137>`__: DOC: typo fixes for discrete Poisson tutorial * `#9139 <https://github.com/scipy/scipy/pull/9139>`__: FIX: Doctest failure in optimize tutorial * `#9143 <https://github.com/scipy/scipy/pull/9143>`__: DOC: missing sigma in Pearson r formula * `#9145 <https://github.com/scipy/scipy/pull/9145>`__: MAINT: Refactor linear programming solvers * `#9149 <https://github.com/scipy/scipy/pull/9149>`__: FIX: Make scipy.odr.ODR ifixx equal to its data.fix if given * `#9156 <https://github.com/scipy/scipy/pull/9156>`__: DOC: special: Mention the sigmoid function in the expit docstring. * `#9160 <https://github.com/scipy/scipy/pull/9160>`__: Fixed a latex delimiter error in levy() * `#9170 <https://github.com/scipy/scipy/pull/9170>`__: DOC: correction / update of docstrings of distributions in scipy.stats * `#9171 <https://github.com/scipy/scipy/pull/9171>`__: better description of the hierarchical clustering parameter * `#9174 <https://github.com/scipy/scipy/pull/9174>`__: domain check for a < b in stats.truncnorm * `#9175 <https://github.com/scipy/scipy/pull/9175>`__: DOC: Minor grammar fix * `#9176 <https://github.com/scipy/scipy/pull/9176>`__: BUG: CloughTocher2DInterpolator: fix miscalculation at neighborless... * `#9177 <https://github.com/scipy/scipy/pull/9177>`__: BUILD: Document the "clean" target in the doc/Makefile. * `#9178 <https://github.com/scipy/scipy/pull/9178>`__: MAINT: make refguide-check more robust for printed numpy arrays * `#9186 <https://github.com/scipy/scipy/pull/9186>`__: MAINT: Remove np.ediff1d occurence * `#9188 <https://github.com/scipy/scipy/pull/9188>`__: DOC: correct typo in extending ndimage with C * `#9190 <https://github.com/scipy/scipy/pull/9190>`__: ENH: Support specifying axes for fftconvolve * `#9192 <https://github.com/scipy/scipy/pull/9192>`__: MAINT: optimize: fixed @pv style suggestions from #9112 * `#9200 <https://github.com/scipy/scipy/pull/9200>`__: Fix make_interp_spline(..., k=0 or 1, axis<0) * `#9201 <https://github.com/scipy/scipy/pull/9201>`__: BUG: sparse.linalg/gmres: use machine eps in breakdown check * `#9204 <https://github.com/scipy/scipy/pull/9204>`__: MAINT: fix up stats.spearmanr and match mstats.spearmanr with... * `#9206 <https://github.com/scipy/scipy/pull/9206>`__: MAINT: include benchmarks and dev files in sdist. * `#9208 <https://github.com/scipy/scipy/pull/9208>`__: TST: signal: bump bsplines test tolerance for complex data * `#9210 <https://github.com/scipy/scipy/pull/9210>`__: TST: mark tests as slow, fix missing random seed * `#9211 <https://github.com/scipy/scipy/pull/9211>`__: ENH: add capability to specify orders in pade func * `#9217 <https://github.com/scipy/scipy/pull/9217>`__: MAINT: Include ``success`` and ``nit`` in OptimizeResult returned... * `#9222 <https://github.com/scipy/scipy/pull/9222>`__: ENH: interpolate: Use scipy.spatial.distance to speed-up Rbf * `#9229 <https://github.com/scipy/scipy/pull/9229>`__: MNT: Fix Fourier filter double case * `#9233 <https://github.com/scipy/scipy/pull/9233>`__: BUG: spatial/distance: fix pdist/cdist performance regression... * `#9234 <https://github.com/scipy/scipy/pull/9234>`__: FIX: Proper suppression * `#9235 <https://github.com/scipy/scipy/pull/9235>`__: BENCH: rationalize slow benchmarks + miscellaneous fixes * `#9238 <https://github.com/scipy/scipy/pull/9238>`__: BENCH: limit number of parameter combinations in spatial.*KDTree... * `#9239 <https://github.com/scipy/scipy/pull/9239>`__: DOC: stats: Fix LaTeX markup of a couple distribution PDFs. * `#9241 <https://github.com/scipy/scipy/pull/9241>`__: ENH: Evaluate plateau size during peak finding * `#9242 <https://github.com/scipy/scipy/pull/9242>`__: ENH: stats: Implement _ppf and _logpdf for crystalball, and do... * `#9246 <https://github.com/scipy/scipy/pull/9246>`__: DOC: Properly render versionadded directive in HTML documentation * `#9255 <https://github.com/scipy/scipy/pull/9255>`__: DOC: mention RootResults in optimization reference guide * `#9260 <https://github.com/scipy/scipy/pull/9260>`__: TST: relax some tolerances so tests pass with x87 math * `#9264 <https://github.com/scipy/scipy/pull/9264>`__: TST Use assert_raises "match" parameter instead of the "message"... * `#9267 <https://github.com/scipy/scipy/pull/9267>`__: DOC: clarify expect() return val when moment is inf/nan * `#9272 <https://github.com/scipy/scipy/pull/9272>`__: DOC: Add description of default bounds to linprog * `#9277 <https://github.com/scipy/scipy/pull/9277>`__: MAINT: sparse/linalg: make test deterministic * `#9278 <https://github.com/scipy/scipy/pull/9278>`__: MAINT: interpolate: pep8 cleanup in test_polyint * `#9279 <https://github.com/scipy/scipy/pull/9279>`__: Fixed docstring for resample * `#9280 <https://github.com/scipy/scipy/pull/9280>`__: removed first check for float in get_sum_dtype * `#9281 <https://github.com/scipy/scipy/pull/9281>`__: BUG: only accept 1d input for bartlett / levene in scipy.stats * `#9282 <https://github.com/scipy/scipy/pull/9282>`__: MAINT: dense_output and t_eval are mutually exclusive inputs * `#9283 <https://github.com/scipy/scipy/pull/9283>`__: MAINT: add docs and do some cleanups in interpolate.Rbf * `#9288 <https://github.com/scipy/scipy/pull/9288>`__: Run distance_transform_edt tests on all types * `#9294 <https://github.com/scipy/scipy/pull/9294>`__: DOC: fix the formula typo * `#9298 <https://github.com/scipy/scipy/pull/9298>`__: MAINT: optimize/trust-constr: restore .niter attribute for backward-compat * `#9299 <https://github.com/scipy/scipy/pull/9299>`__: DOC: clarification of default rvs method in scipy.stats * `#9301 <https://github.com/scipy/scipy/pull/9301>`__: MAINT: removed unused import sys * `#9302 <https://github.com/scipy/scipy/pull/9302>`__: MAINT: removed unused imports * `#9303 <https://github.com/scipy/scipy/pull/9303>`__: DOC: signal: Refer to fs instead of nyq in the firwin docstring. * `#9305 <https://github.com/scipy/scipy/pull/9305>`__: ENH: Added Yeo-Johnson power transformation * `#9306 <https://github.com/scipy/scipy/pull/9306>`__: ENH - add dual annealing * `#9309 <https://github.com/scipy/scipy/pull/9309>`__: ENH add the yulesimon distribution to scipy.stats * `#9317 <https://github.com/scipy/scipy/pull/9317>`__: Nested SLSQP bug fix. * `#9320 <https://github.com/scipy/scipy/pull/9320>`__: MAINT: stats: avoid underflow in stats.geom.ppf * `#9326 <https://github.com/scipy/scipy/pull/9326>`__: Add example for Rosenbrock function * `#9332 <https://github.com/scipy/scipy/pull/9332>`__: Sort file lists * `#9340 <https://github.com/scipy/scipy/pull/9340>`__: Fix typo in find_peaks documentation * `#9343 <https://github.com/scipy/scipy/pull/9343>`__: MAINT Use np.full when possible * `#9344 <https://github.com/scipy/scipy/pull/9344>`__: DOC: added examples to docstring of dirichlet class * `#9346 <https://github.com/scipy/scipy/pull/9346>`__: DOC: Fix import of scipy.sparse.linalg in example (#9345) * `#9350 <https://github.com/scipy/scipy/pull/9350>`__: Fix interpolate read only * `#9351 <https://github.com/scipy/scipy/pull/9351>`__: MAINT: special.erf: use the x->-x symmetry * `#9356 <https://github.com/scipy/scipy/pull/9356>`__: Fix documentation typo * `#9358 <https://github.com/scipy/scipy/pull/9358>`__: DOC: improve doc for ksone and kstwobign in scipy.stats * `#9362 <https://github.com/scipy/scipy/pull/9362>`__: DOC: Change datatypes of A matrices in linprog * `#9364 <https://github.com/scipy/scipy/pull/9364>`__: MAINT: Adds implicit none to fftpack fortran sources * `#9369 <https://github.com/scipy/scipy/pull/9369>`__: DOC: minor tweak to CoC (updated NumFOCUS contact address). * `#9373 <https://github.com/scipy/scipy/pull/9373>`__: Fix exception if python is called with -OO option * `#9374 <https://github.com/scipy/scipy/pull/9374>`__: FIX: AIX compilation issue with NAN and INFINITY * `#9376 <https://github.com/scipy/scipy/pull/9376>`__: COBLYA -> COBYLA in docs * `#9377 <https://github.com/scipy/scipy/pull/9377>`__: DOC: Add examples integrate: fixed_quad and quadrature * `#9379 <https://github.com/scipy/scipy/pull/9379>`__: MAINT: TST: Make tests NumPy 1.8 compatible * `#9385 <https://github.com/scipy/scipy/pull/9385>`__: CI: On Travis matrix "OPTIMIZE=-OO" flag ignored * `#9387 <https://github.com/scipy/scipy/pull/9387>`__: Fix defaut value for 'mode' in 'ndimage.shift' in the doc * `#9392 <https://github.com/scipy/scipy/pull/9392>`__: BUG: rank has to be integer in rank_filter: fixed issue 9388 * `#9399 <https://github.com/scipy/scipy/pull/9399>`__: DOC: Misc. typos * `#9400 <https://github.com/scipy/scipy/pull/9400>`__: TST: stats: Fix the expected r-value of a linregress test. * `#9405 <https://github.com/scipy/scipy/pull/9405>`__: BUG: np.hstack does not accept generator expressions * `#9408 <https://github.com/scipy/scipy/pull/9408>`__: ENH: linalg: Shorter ill-conditioned warning message * `#9418 <https://github.com/scipy/scipy/pull/9418>`__: DOC: Fix ndimage docstrings and reduce doc build warnings * `#9421 <https://github.com/scipy/scipy/pull/9421>`__: DOC: Add missing docstring examples in scipy.spatial * `#9422 <https://github.com/scipy/scipy/pull/9422>`__: DOC: Add an example to integrate.newton_cotes * `#9427 <https://github.com/scipy/scipy/pull/9427>`__: BUG: Fixed defect with maxiter #9419 in dual annealing * `#9431 <https://github.com/scipy/scipy/pull/9431>`__: BENCH: Add dual annealing to scipy benchmark (see #9415) * `#9435 <https://github.com/scipy/scipy/pull/9435>`__: DOC: Add docstring examples for stats.binom_test * `#9443 <https://github.com/scipy/scipy/pull/9443>`__: DOC: Fix the order of indices in optimize tutorial * `#9444 <https://github.com/scipy/scipy/pull/9444>`__: MAINT: interpolate: use operator.index for checking/coercing... * `#9445 <https://github.com/scipy/scipy/pull/9445>`__: DOC: Added missing example to stats.mstats.kruskal * `#9446 <https://github.com/scipy/scipy/pull/9446>`__: DOC: Add note about version changed for jaccard distance * `#9447 <https://github.com/scipy/scipy/pull/9447>`__: BLD: version-script handling in setup.py * `#9448 <https://github.com/scipy/scipy/pull/9448>`__: TST: skip a problematic linalg test * `#9449 <https://github.com/scipy/scipy/pull/9449>`__: TST: fix missing seed in lobpcg test. * `#9456 <https://github.com/scipy/scipy/pull/9456>`__: TST: test_eigs_consistency() now sorts output Checksums ========= MD5 ~~~ 0bb53a49e77bca11fb26698744c60f97 scipy-1.2.0-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 39e215ac7e8d6de33d939486987dcba4 scipy-1.2.0-cp27-cp27m-manylinux1_i686.whl d6e33b2c05ffbcf9790628d656c8e61f scipy-1.2.0-cp27-cp27m-manylinux1_x86_64.whl a463a12d77b87df0a2d202323771a908 scipy-1.2.0-cp27-cp27m-win32.whl 3dc17a11c7dd211ce51a338cfe30eb48 scipy-1.2.0-cp27-cp27m-win_amd64.whl 82b1ecbecadfeddd2be1c6d616491029 scipy-1.2.0-cp27-cp27mu-manylinux1_i686.whl 65021ade783f1416b1920d2a2cc39d4d scipy-1.2.0-cp27-cp27mu-manylinux1_x86_64.whl 7cc2cdbc9b421ef10695b898cdc241e7 scipy-1.2.0-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 2a4ccbfcccb9395fa7554a82db40e454 scipy-1.2.0-cp34-cp34m-manylinux1_i686.whl 662dc35acd6f588565cd6467465fc742 scipy-1.2.0-cp34-cp34m-manylinux1_x86_64.whl a19bd9969bb5b92595e82d924b8272f9 scipy-1.2.0-cp34-cp34m-win32.whl 5f1eaa3745956db0da724f83dd174559 scipy-1.2.0-cp34-cp34m-win_amd64.whl d32a4c31d0a188f3550c1306d20b03c7 scipy-1.2.0-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 34f4a7f04abfda87fac8ab38a9a70a77 scipy-1.2.0-cp35-cp35m-manylinux1_i686.whl bd4d56910802870072e3c5ded69a8f08 scipy-1.2.0-cp35-cp35m-manylinux1_x86_64.whl cb3fb7ddd3992928f9173e4eb489d23e scipy-1.2.0-cp35-cp35m-win32.whl 68f5ddcb6e592b1d9cba95f24faee7b5 scipy-1.2.0-cp35-cp35m-win_amd64.whl c0e110f3a935731782c96a13cc264ea2 scipy-1.2.0-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 9d898924498abbe2d26dd18b3413fb11 scipy-1.2.0-cp36-cp36m-manylinux1_i686.whl dd9ae664cbe7de54828d83c772e24da3 scipy-1.2.0-cp36-cp36m-manylinux1_x86_64.whl 8dea8432610bb3c63114eb6469c5f99a scipy-1.2.0-cp36-cp36m-win32.whl ebda830aec7b60193772741f85fee28c scipy-1.2.0-cp36-cp36m-win_amd64.whl 0e8b7a7908c50e635e639d2c69901140 scipy-1.2.0-cp37-cp37m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl 56719437a821f9f2f98f069225e70c87 scipy-1.2.0-cp37-cp37m-manylinux1_i686.whl 93d9d978855516ec38fa08620ef3443c scipy-1.2.0-cp37-cp37m-manylinux1_x86_64.whl fff3d66b877b6e6b9984b84ae9e4d76c scipy-1.2.0-cp37-cp37m-win32.whl 3defb2c8b2f69057919ee3b0c92de65c scipy-1.2.0-cp37-cp37m-win_amd64.whl e57011507865b0b702aff6077d412e03 scipy-1.2.0.tar.gz 8eb6c1d7ceae0d06aef474f7801b8fca scipy-1.2.0.tar.xz b0fb16b09319d3031d27ccf21a3ef474 scipy-1.2.0.zip SHA256 ~~~~~~ d1ae77b79fd5e27a10ba7c4e7c3a62927b9d29a4dccf28f6905c25d983aaf183 scipy-1.2.0-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 4b1f0883cb9d8ee963cf0a31c87341e9e758abb2cf1e5bcc0d7b066ef6b17573 scipy-1.2.0-cp27-cp27m-manylinux1_i686.whl c5eae911cf26b3c7eda889ec98d3c226f312c587acfaaf02602473f98b4c16d6 scipy-1.2.0-cp27-cp27m-manylinux1_x86_64.whl 58f0435f052cb60f1472c77f52a8f6642f8877b70559e5e0b9a1744f33f5cbe5 scipy-1.2.0-cp27-cp27m-win32.whl 4cce25c6e7ff7399c67dfe1b5423c36c391cf9b4b2be390c1675ab410f1ef503 scipy-1.2.0-cp27-cp27m-win_amd64.whl 02cb79ea38114dc480e9b08d6b87095728e8fb39b9a49b449ee443d678001611 scipy-1.2.0-cp27-cp27mu-manylinux1_i686.whl 7dc4002f0a0a688774ef04878afe769ecd1ac21fe9b4b1d7125e2cf16170afd3 scipy-1.2.0-cp27-cp27mu-manylinux1_x86_64.whl 7994c044bf659b0a24ad7673ec7db85c2fadb87e4980a379a9fd5b086fe3649a scipy-1.2.0-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 72bd766f753fd32f072d30d7bc2ad492d56dbcbf3e13764e27635d5c41079339 scipy-1.2.0-cp34-cp34m-manylinux1_i686.whl 3132a9fab3f3545c8b0ba15688d11857efdd4a58d388d3785dc474f56fea7138 scipy-1.2.0-cp34-cp34m-manylinux1_x86_64.whl 7413080b381766a22d52814edb65631f0e323a7cea945c70021a672f38acc73f scipy-1.2.0-cp34-cp34m-win32.whl 6f791987899532305126309578727c0197bddbafab9596bafe3e7bfab6e1ce13 scipy-1.2.0-cp34-cp34m-win_amd64.whl 937147086e8b4338bf139ca8fa98da650e3a46bf2ca617f8e9dd68c3971ec420 scipy-1.2.0-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 63e1d5ca9e5e1984f1a275276991b036e25d39d37dd7edbb3f4f6165c2da7dbb scipy-1.2.0-cp35-cp35m-manylinux1_i686.whl 03c827cdbc584e935264040b958e5fa0570a16095bee23a013482ba3f0e963a2 scipy-1.2.0-cp35-cp35m-manylinux1_x86_64.whl bc294841f6c822714af362095b181a853f47ed5ce757354bd2e4776d579ff3a4 scipy-1.2.0-cp35-cp35m-win32.whl cca33a01a5987c650b87a1a910aa311ffa44e67cca1ff502ef9efdae5d9a8624 scipy-1.2.0-cp35-cp35m-win_amd64.whl 8608316d0cc01f8b25111c8adfe6efbc959cbba037a62c784551568d7ffbf280 scipy-1.2.0-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 fb36064047e6bf87b6320a9b6eb7f525ef6863c7a4aef1a84a4bbfb043612617 scipy-1.2.0-cp36-cp36m-manylinux1_i686.whl 854bd87cc23824d5db4983956bc30f3790e1c7448f1a9e6a8fb7bff7601aef87 scipy-1.2.0-cp36-cp36m-manylinux1_x86_64.whl fc1a19d95649439dbd50baca676bceb29bbfcd600aed2c5bd71d9bad82a87cfe scipy-1.2.0-cp36-cp36m-win32.whl 8f5fcc87b48fc3dd6d901669c89af4feeb856dffb6f671539a238b7e8af1799c scipy-1.2.0-cp36-cp36m-win_amd64.whl bc6a88b0009a1b60eab5c22ac3a006f6968d6328de10c6a64ebb0d64a05548d3 scipy-1.2.0-cp37-cp37m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl 64b2c35824da3ef6bb1e722216e4ef28802af6413c7586136500e343d34ba179 scipy-1.2.0-cp37-cp37m-manylinux1_i686.whl 78a67ee4845440e81cfbfabde20537ca12051d0eeac951fe4c6d8751feac3103 scipy-1.2.0-cp37-cp37m-manylinux1_x86_64.whl 04f2b23258139c109d0524f111597dd095a505d9cb2c71e381d688d653877fa3 scipy-1.2.0-cp37-cp37m-win32.whl 5706b785ca289fdfd91aa05066619e51d140613b613e35932601f2315f5d8470 scipy-1.2.0-cp37-cp37m-win_amd64.whl 51a2424c8ed80e60bdb9a896806e7adaf24a58253b326fbad10f80a6d06f2214 scipy-1.2.0.tar.gz 1cfafc64e1c1c0b6d040d35780ba06f01e9cb44f3bacd8f33a5df6efe5a144a6 scipy-1.2.0.tar.xz 5b3d4b1654b4723431ad7f47b4556498a86a53d2fdb86e85a45c3da0c2a72a4b scipy-1.2.0.zip -----BEGIN PGP SIGNATURE----- iQIzBAEBCAAdFiEEgfAqrOuERrV4fwr6XCLdIy4FKasFAlwYY1YACgkQXCLdIy4F KauihhAAo0fk+a4U4ilFXeZrUTuZTeqqHtD60yV8Z2pZvqQyH1BBP8BKksV/v3bP 6ckl+upWaolMjZgehWhAKyGgE2oUHDu6RxaRS97Kbbns580M+wWduPX7kjf7OKoX KFX1a8/GlBWTQKIMj6P2oShT7rvYB+WABvyoTxXKbHG0/ArOtOqqAYJFKLeOYB6m o0+qajqt9syV2iqBFlHnrPKQ8qjtfPxqCP9KHhNIbHd17305YwJc58CBhpulIjaP HIhJOuP7xigELX9yCzJ2qFGBjTd2HNvBQWIRjNDfbox6mhWO4no30c+OamG3MAr7 n9TDzSIjxkRedvBzMRJwA/Q5/Mou/R16BF+ZzvCVZnp/h6LrXQg4ENfR304Byyy3 NlYzmQKlV0XvP4oYewBnLfq6hcXAum7rf3L8ene8mu0OWJumW7Yr6PWfDNECDKvX sPWSwkNu01Pzg/KUUkrS9w6m9bZTj4UP15L2Z8JSVdp/wxCHGh8txJWjOenoZBnD BPyitzsgbuW+pd7+WmjZoJpr8QjL/Uw8vpUwlvKAvvVfFnVeVh3X1awK+D6iba69 LbyfhOG3R846WkxHh458uvNxmYUbm6sqaZ7lzY91bD8z61jS6PCvpR8BJcwmpP7Y q/duWsaakRWLe35CE8KuvKCgVFhhStX2sZ/hRkNsvm2/hEAtNW0= =3Enc -----END PGP SIGNATURE-----

2 1

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

NumPy-Discussion December 2018