Mailman 3 November 2018 - NumPy-Discussion

mixed mode arithmetic
by Neal Becker 11 Jul '23

11 Jul '23

I've been browsing the numpy source. I'm wondering about mixed-mode arithmetic on arrays. I believe the way numpy handles this is that it never does mixed arithmetic, but instead converts arrays to a common type. Arguably, that might be efficient for a mix of say, double and float. Maybe not. But for a mix of complex and a scalar type (say, CDouble * Double), it's clearly suboptimal in efficiency. So, do I understand this correctly? If so, is that something we should improve?

4 6

Invalid value encoutered : how to prevent numpy.where to do this?
by Eric Emsellem 18 Feb '23

18 Feb '23

Dear all, I have a code using lots of "numpy.where" to make some constrained calculations as in: data = arange(10) result = np.where(data == 0, 0., 1./data) # or data1 = arange(10) data2 = arange(10)+1.0 result = np.where(data1 > data2, np.sqrt(data1-data2), np.sqrt(data2-data2)) which then produces warnings like: /usr/bin/ipython:1: RuntimeWarning: invalid value encountered in sqrt or for the first example: /usr/bin/ipython:1: RuntimeWarning: divide by zero encountered in divide How do I avoid these messages to appear? I know that I could in principle use numpy.seterr. However, I do NOT want to remove these warnings for other potential divide/multiply/sqrt etc errors. Only when I am using a "where", to in fact avoid such warnings! Note that the warnings only happen once, but since I am going to release that code, I would like to avoid the user to get such messages which are irrelevant here (because I am testing, with the where, when NOT to divide by zero or take a sqrt of a negative number). thanks! Eric

5 4

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

How to get sources of the current state of 1.16?
by Pavlyk, Oleksandr 30 Nov '18

30 Nov '18

Are they the same as the current master, or is there a mechanism to query issue tracker for all PRs designated to make it into 1.16? Even then, what should the base be? Perhaps this is already documented in either an issue or a PR. Thanks for the pointer. Sasha

3 4

Reminder: Numpy dev meeting Fri-Sat Nov 30-Dec 1
by Matti Picus 29 Nov '18

29 Nov '18

We will be meeting at BIDS 9:00AM Friday for a two-day NumPy developer meeting. All are welcome, if you haven't already please let Stefan know you are coming so we can plan for space. A tentative schedule is here https://hackmd.io/gFqjPUSvSmm-0gmBDbrTBw?both Feel free to add content (just tag it with your name). Tyler, Matti, Stefan.

5 5

Weekly status meeting Nov 28 at 12:00 Pacific time
by Matti Picus 27 Nov '18

27 Nov '18

We will be holding our weekly BIDS NumPy status meeting on Wed Nov 28 at noon Pacific time. Please join us. The draft agenda, along with details of how to join, is up at https://hackmd.io/Gn1ymjwkRjm9WVY5Cgbwsw?both Previous sessions' notes are available at https://github.com/BIDS-numpy/docs/tree/master/status_meetings Matti, Tyler and Stefan

1 0

NumPy 1.16
by Charles R Harris 27 Nov '18

27 Nov '18

Hi All, Just an update of the NumPy 1.16 release schedule. The last PR milestoned for the release is #12219 <https://github.com/numpy/numpy/pull/12219>, and it is about done. The current release blocker is the upcoming OpenBLAS 0.3.4, which should fix the reported threading problems, but if it doesn't come out in the next month we might want to revisit that. The OpenBLAS issues for 0.3.4 are here <https://github.com/xianyi/OpenBLAS/issues?q=is%3Aopen+is%3Aissue+milestone%…>. Meanwhile, other PR's continue to go in. Chuck

2 2

ANN: SciPy 1.2.0rc1 -- please test
by Tyler Reddy 26 Nov '18

26 Nov '18

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA256 Hi all, On behalf of the SciPy development team I'm pleased to announce the release candidate SciPy 1.2.0rc1. Please help us test out this release candidate -- the 1.2.x series will be an LTS release and the last to support Python 2.7. Sources and binary wheels can be found at: https://pypi.python.org/pypi/scipy and at: https://github.com/scipy/scipy/releases/tag/v1.2.0rc1 To install the release candidate with pip: pip install scipy==1.2.0rc1 ========================== SciPy 1.2.0 Release Notes ========================== Note: Scipy 1.2.0 is not released yet! 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 + * 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 - ----------------------- * `#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 - ----------------------- * `#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 ~~~ 47d309402d2e5574be8fa261fadfaf58 scipy-1.2.0rc1-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 911dfde5be66403c07c60e19aa631dc2 scipy-1.2.0rc1-cp27-cp27m-manylinux1_i686.whl a693189336365595b42b0d93f825b826 scipy-1.2.0rc1-cp27-cp27m-manylinux1_x86_64.whl ec5abd33480761ed9701f7fd2274fc47 scipy-1.2.0rc1-cp27-cp27m-win32.whl bc3d40311f057b12f8fea97166ef8112 scipy-1.2.0rc1-cp27-cp27m-win_amd64.whl 33848233e6438b1ff9183d8a4794daed scipy-1.2.0rc1-cp27-cp27mu-manylinux1_i686.whl c2cb1166ce0071e1fe42ed1c3e60b75e scipy-1.2.0rc1-cp27-cp27mu-manylinux1_x86_64.whl a04b5a758f555e05e6431b8a1f035888 scipy-1.2.0rc1-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 ad511246d0742cf0669fedf292cc01bb scipy-1.2.0rc1-cp34-cp34m-manylinux1_i686.whl 15aa08ef43a6c5cb320bc015f01087ad scipy-1.2.0rc1-cp34-cp34m-manylinux1_x86_64.whl 86a59b81a3e6894d9054139311a2c51f scipy-1.2.0rc1-cp34-cp34m-win32.whl 8b6e33253579916ea873c45989ee5bea scipy-1.2.0rc1-cp34-cp34m-win_amd64.whl db7a4de02828471bf9f800814ff68627 scipy-1.2.0rc1-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 c296b6270d29d3ec6dafddf8ceae67fb scipy-1.2.0rc1-cp35-cp35m-manylinux1_i686.whl 3ba7a825f61822128d960fa728010e51 scipy-1.2.0rc1-cp35-cp35m-manylinux1_x86_64.whl c083c8287da110b707d181f6638ce122 scipy-1.2.0rc1-cp35-cp35m-win32.whl 2242eac92681085258535ed96bd040d7 scipy-1.2.0rc1-cp35-cp35m-win_amd64.whl d5c238903c00e91a40d56023f1a27ed4 scipy-1.2.0rc1-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 e8ab7487a9a53b86a6510772c45af787 scipy-1.2.0rc1-cp36-cp36m-manylinux1_i686.whl 9991b5958d736488bef638eea463945d scipy-1.2.0rc1-cp36-cp36m-manylinux1_x86_64.whl 9c108a9d7e967b8c9a5e5143b1a15b40 scipy-1.2.0rc1-cp36-cp36m-win32.whl 54d63041f0315d341d9ffb028a98e767 scipy-1.2.0rc1-cp36-cp36m-win_amd64.whl 534276c864ab3139811561c022608cc3 scipy-1.2.0rc1-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 4bd26c179a10891087bd81a658573683 scipy-1.2.0rc1-cp37-cp37m-manylinux1_i686.whl 1a3170b4f1df42f28efbe197e54eb9a3 scipy-1.2.0rc1-cp37-cp37m-manylinux1_x86_64.whl 72b89f7e2c1d13dc8dbb21600fb184da scipy-1.2.0rc1-cp37-cp37m-win32.whl 433ef294c4a015da0a6e0f063289a658 scipy-1.2.0rc1-cp37-cp37m-win_amd64.whl 83abb1befce326916e0435d428b36e62 scipy-1.2.0rc1.tar.gz 21b7570fb577543807feb4b4c1fdad8a scipy-1.2.0rc1.tar.xz 11221dc23e0d3b316b5564f2f435aaf1 scipy-1.2.0rc1.zip SHA256 ~~~~~~ a3282027743e89d5fcb6cd7a9e4ecdbbde61bf8126fd19683e97b69f5f6a2163 scipy-1.2.0rc1-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 8b8841e03620d4d704f4efd3daed575e23a6272ae9c9be2c4cc751febee984f7 scipy-1.2.0rc1-cp27-cp27m-manylinux1_i686.whl 254ea2f7f3c5afef9f02c0192d2dbd8f5336c1b1c53efae7ef64a8880cccc299 scipy-1.2.0rc1-cp27-cp27m-manylinux1_x86_64.whl d449832419df3e37a3942778b46c140fd61d1e4f38f9e34bed278a77aacdbd31 scipy-1.2.0rc1-cp27-cp27m-win32.whl e57f3e3eaa88cd7bc93466122fca48c36863e03aeb24f9d570a2e6b2ea3cbf92 scipy-1.2.0rc1-cp27-cp27m-win_amd64.whl 00dcb606101fa10951ee235af69dbff55d999b01c9bb2bc0f64df5fc3aff4eb6 scipy-1.2.0rc1-cp27-cp27mu-manylinux1_i686.whl 38857eb49f7e38d3ec079772225a79235d0bd847e24d2fa8c9a9fa70ee69f0a2 scipy-1.2.0rc1-cp27-cp27mu-manylinux1_x86_64.whl 9b9950278f36c4e7779148cc9a719d41a305024c70005f994412845172ade346 scipy-1.2.0rc1-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 c8a2fc15c64f1b162b02f86d28153b65f64720ca64e832bcf5bfae060a507949 scipy-1.2.0rc1-cp34-cp34m-manylinux1_i686.whl 4e205e0e5e88fe2105c594a706ac9fa558fe7a4daa2bef2c86b343ab507d8bd6 scipy-1.2.0rc1-cp34-cp34m-manylinux1_x86_64.whl b04fb8ba796198d83614138d7f96e88791d83f5f4e31e628cce51fda1a092f66 scipy-1.2.0rc1-cp34-cp34m-win32.whl c34e4ce9a8c62c85f322d7897a257a839a4bb7fd94e08701044e9b1cc1bb15a6 scipy-1.2.0rc1-cp34-cp34m-win_amd64.whl cb7f4c289c06514c6e224263868a98d5e6aa6e8a90f2304b16ff276aa96030ce scipy-1.2.0rc1-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 dcaeed34e7e965e935ba252fd3290c04cafb7d84f6f44c9c08269504aa2e0a05 scipy-1.2.0rc1-cp35-cp35m-manylinux1_i686.whl 13b234b4fbda1368474a667fc29e8365b622c4c607ed490f00643625c8f50cca scipy-1.2.0rc1-cp35-cp35m-manylinux1_x86_64.whl 073560d6598b97b8f214a31856a1d43c96701b46c5cc9805789f962c3b8a0e97 scipy-1.2.0rc1-cp35-cp35m-win32.whl 600cf596b83f185dca3455d03ca802de0b4de98cb4c8d041226089d78d04c2bc scipy-1.2.0rc1-cp35-cp35m-win_amd64.whl 77b7ed8e13f03a843d2f11bd140f224abe62fb56656f35348578e3838ff4d528 scipy-1.2.0rc1-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 98786b516d1955592f921c2b36505840ff921c9e82f5c28a6c2a580fb3e96af1 scipy-1.2.0rc1-cp36-cp36m-manylinux1_i686.whl 8f62fc6ac6b842d7c3c499269150ec81d26a0dea5ce818aef3b0e9e14e53c5c7 scipy-1.2.0rc1-cp36-cp36m-manylinux1_x86_64.whl 213cfc35ec2fbf86c5957d1ada99a1fe1eacccdb498d4790089af9cf50cadab4 scipy-1.2.0rc1-cp36-cp36m-win32.whl 287e46f87399eb9897c726c6d358c893f6c769300599bc5da95cbf3397a00aa7 scipy-1.2.0rc1-cp36-cp36m-win_amd64.whl 7116d193da4baca6f7cd1cd7810548fa03ed4a06e05f325978b2f0018b05ead9 scipy-1.2.0rc1-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 9cd3fe767225b0dcfcc9f09ed18288b51c29b35ca16dc4e5a6f3587b4ac7d7b5 scipy-1.2.0rc1-cp37-cp37m-manylinux1_i686.whl 4024aea96a01ca05af701f93fd227a7f646258116903899a75b1f4a1f0134bf8 scipy-1.2.0rc1-cp37-cp37m-manylinux1_x86_64.whl eccd0e6d321585b6e2ee18fa0ee1db4fe042e52fb4ae8e28a3d223df6bcd6a8e scipy-1.2.0rc1-cp37-cp37m-win32.whl 96f6c69b2d8f63dad5612385521d0cf8b62f665a62f56b6b3d3fb7042a63c34c scipy-1.2.0rc1-cp37-cp37m-win_amd64.whl ca9ba36bb271dcb4273e330c0bd418a1c3566ff76248dd634efecbe0e9c1721c scipy-1.2.0rc1.tar.gz b8fe757f377b43c733a0ba235b990fb4b3722bd6f5930a26359d16752e94560c scipy-1.2.0rc1.tar.xz da1980e7e037e2275821d7611a91eadafdc157b096f36f41d05cc0ea4ae539bc scipy-1.2.0rc1.zip -----BEGIN PGP SIGNATURE----- iQIzBAEBCAAdFiEEgfAqrOuERrV4fwr6XCLdIy4FKasFAlv8LB8ACgkQXCLdIy4F KauhWw//cFUboxtuNZeEh98Pn2mfVIqRF8hc6VK7jeXU64qiAdOnbBRKXfoXWpbt 8fEckTFVVt2CQzIEZju429P0eWv8psfvn90cNnj5VHz7h46AH5gQee/cfP1kNWGO mxcYczqZN8+ldDnww4hfk0R+3IrpfmQbVSMEqBF84uottfgUjUzPtcFHJmmmcOF2 7XWhAP5IWMctcwq3r3WAa8yLUmlEIB3Fs9mPPr1We7e0QiXnEueUrXVZF9Oz3UpJ UFiTwX5nxnNdxZJwtLbBXhxJUbvPnooby0x1NwxFG6AwrWaPG3my0e9kIzpwqfsf 0B7Pt/xPEHWIizaqMyfUiaS7FZOUfvq6xs5aCyQeP9Wtnwherm6SQ/Qs+LFSb8E2 9cgyFmaBeslrTMd3f1YMcb6d1hmNSjbBmWccZEXCqjC9UClxbAQDHDcdmTl+4vp7 xATCxWkYVP9lOLH5TtIC1Bo0AZXiOxI3JnojQS65JH0w4unh1IJJkENurzCATh4/ vnyddy0x2e1ok5bX8fpVFrCc8lSdO8aDY5cYEU55XZhkyroet3IaOYqwPzPQPwoJ QgYEsqxa2Urn0faV5uuDFTscnCAHK0EVXhr3yyL0YoIGOPffM8JaswEAUaFwLrq0 KEbOavdImtSwqL9v6x5tDyYXh/mHQ70GGtPS68iSc1NexRrwLAw= =F/+Z -----END PGP SIGNATURE-----

2 1

lstsq underdetermined behaviour
by Romesh Abeysuriya 19 Nov '18

19 Nov '18

Hi all, I'm solving an underdetermined system using `numpy.linalg.lstsq` and trying to track down its behavior for underdetermined systems. In previous versions of numpy (e.g. 1.14) in `linalg.py` the definition for `lstsq` calls `dgelsd` for real inputs, which I think means that the underdetermined system is solved with the minimum-norm solution (that is, minimizing the norm of the solution vector, in addition to minimizing the residual). In 1.15 the call is instead to `_umath_linalg.lstsq_m` and I'm not sure what this actually ends up doing - does this end up being the same as `dgelsd`? If so, it would be great if the documentation for `numpy.linalg.lstsq` stated that it is returning the minimum-norm solution (as it stands, it reads as undefined, so in theory I don't think one can rely on any particular solution being returned for an underdetermined system) Cheers, Romesh

3 3

Vectorized version of numpy.linspace
by Stephan Hoyer 15 Nov '18

15 Nov '18

It recently came up on GitHub (at part of the discussion in https://github.com/numpy/numpy/issues/12379) that numpy.linspace could, at least in principle, be modified to support array inputs: It looks like this has been requested on StackOverflow, too: https://stackoverflow.com/questions/46694167/vectorized-numpy-linspace-acro… My tentative proposal: - "start" and "stop" are broadcast against each other to form start/stop arrays. (Or we could require that start/stop have matching shape.) - A new dimension of size "num" is inserted into the result, either along the first or last axis. - A new keyword argument "axis" could control where the axis is inserted in the result. - Vectorization support should be added in the same way to geomspace and logspace. Does this seem like a good idea? It's a relatively simple generalization, and one that I, at least, would find useful (I can think of a use-case in my own code that came up just last week). I doubt I'll have time to implement this myself in the near future, but I thought I would get the discussion going -- this might be a good project for a new contributor to work on. Cheers, Stephan

7 10