NumPy-Discussion April 2019

numpy-discussion@python.org

38 participants
33 discussions

Re: [Numpy-discussion] example reading binary Fortran file

by Neil Martinsen-Burrell

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

4 months, 2 weeks

Adding to the non-dispatched implementation of NumPy methods

by Stephan Hoyer

Hi everyone, We have a proposed a revision to NEP-18 (__array_function__). The proposal is for an adding an alias to the non-dispatched version of NumPy array functions in the __numpy_implementaiton__ function attribute: https://github.com/numpy/numpy/pull/13305 I believe this attribute improves the protocol in three ways: 1. It provides a hook that __array_function__ methods can use to call implementation intended for NumPy arrays. This allows for "partial implementations" of NumPy's API, which turns out to useful even for some array libraries that reimplement nearly everything (namely, for CuPy and JAX). 2. It allows for fast access to the non-dispatching version of NumPy functions, e.g., np.concatenate.__numpy_implementation__(list_of_all_numpy_arrays). 3. Internally, the implementation of numpy.ndarray.__array_function__ now looks identical to how we encourage outside developers to write their own __array_function__ methods. The dispatching logic no longer includes a special case for NumPy arrays. Feedback would be greatly welcomed! Best, Stephan

2 years, 6 months

Re: [Numpy-discussion] grant proposal for core scientific Python projects (rejected)

by Joe Harrington

Hi Ralf, The rejection is disappointing, for sure. Some good ammo for next time might be the recommendations in this report from the US National Academies of Science, Engineering, and Medicine: http://sites.nationalacademies.org/SSB/CurrentProjects/SSB_178892 https://www.nap.edu/read/25217/chapter/1#ii You can download a free PDF if you click around and give them an email address. There is some code on the cover that might raise a smile. Lorena Barba, Kelle Cruz, and many other community members contributed, both as committee members and as white-paper authors. While it's a report for NASA, the conclusions are strong and there is explicit support of investment in community resources like numpy, scipy, astropy, matplotlib, etc. Other agencies are asking similar questions, so I expect the report to get somewhat of a look at NSF, etc. A note on the Academies' process: A consensus study report must only include statements that no single member of the committee objects to, and the committee generally only includes senior members or people with a lot of relevant experience. There were stakeholders from some large modeling shops for whom openness might be a threat to their business model, in their eyes, and some of the senior members had never experienced the open-source environment and had bad experiences sharing software. This made for an interesting social dynamic, and prevented an all-out recommendation to forcibly open everything immediately. Given all that, I was ultimately pleased that we got full agreement on the recommendations we did make. So, some general thoughts on fundraising, not specific to this proposal: 1. Try NASA. The Administrator for Space Science, Thomas Zurbuchen, is pushing "open" very hard, given the success of open data in NASA Earth Science, and its positive impact on the economy in fields like agriculture and weather forecasting. He paid for the study above. Many grant programs specifically solicit proposals for open-source tools. There are also technology development programs in other parts of NASA than the Science Mission Directorate. Try contacting Dr. Michael New, who is Zurbuchen's deputy, and could direct you to appropriate programs. (Please, let's be coordinated and not all deluge the guy.) 2. As suggested in another message, it's often easier to get support for a specific, targeted item as part of a big project or institute using that item, such as LSST or the black-hole group. There's a certain way to wend into those projects, usually originating from within. STScI has long devoted programmer resources, for example. 3. There's such a thing as a share-in-savings contract at NASA, in which you calculate a savings, such as from avoided costs of licensing IDL or Matlab, and say you'll develop a replacement for that product that costs less, in exchange for a portion of the savings. These are rare and few people know about them, but one presenter to the committee did discuss them and thought they'd be appropriate. I've always felt that we could get a chunk of change this way, and was surprised to find that the approach exists and has a name. About 3 of 4 people I talk to at NASA have no idea this even exists, though, and I haven't pursued it to its logical end to see if it's viable. 4. I mostly lurk here, since being more actively involved in the early days of numpy docs, so maybe this one has been tried already, or is in the works. My apologies if so. Think of development as a product to buy. You could put chunks of development up for sale, advertise them, and coordinate one or more groups buying them together. For something like an efficiency boost, you could price it according to the avoided cost of CPU resources for a project of a given size (e.g., somewhat below the net present value of avoided future AWS cycles for the projects buying it). It would be like buying a custom-built data pipeline, except that once you buy it, everyone gets it. This might mean scoping out a roadmap of improvements, packaging them into fundable projects with teams ready to go, pricing them, advertising them to specific customers and in trade media and shows, and making sales pitches. This sounds really weird to us scientists, but it would work just like a regular purchase for services, which the government and industry are much more used to doing than donations to open-source projects. Don't just sell what the customer is buying, sell in the manner that the customer likes to buy. 5. And, keep trying grant proposals to NSF! --jh-- On 4/18/19 6:36 AM, Ralf Gommers <ralf.gommers(a)gmail.com> wrote: > > A number of core projects (NumPy, SciPy, Matplotlib, Pandas, > scikit-learn) got together and put in a proposal to NSF for a large 5 > year grant, and it was unfortunately just rejected. We now published > the proposal, which may be of interest: > https://figshare.com/articles/Mid-Scale_Research_Infrastructure_-_The_Scien….

2 years, 7 months

ANN: SciPy 1.3.0rc1 -- please test

by Tyler Reddy

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Hi all, On behalf of the SciPy development team I'm pleased to announce the release candidate SciPy 1.3.0rc1. Please help us test this pre-release. Sources and binary wheels can be found at:https://pypi.org/project/scipy/ and at:https://github.com/scipy/scipy/releases/tag/v1.3.0rc1 One of a few ways to install the release candidate with pip: pip install scipy==1.3.0rc1 ========================== SciPy 1.3.0 Release Notes ========================== Note: Scipy 1.3.0 is not released yet! SciPy 1.3.0 is the culmination of 5 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been some 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 1.3.x branch, and on adding new features on the master branch. This release requires Python 3.5+ and NumPy 1.13.3 or greater. For running on PyPy, PyPy3 6.0+ and NumPy 1.15.0 are required. Highlights of this release -------------------------- - Three new ``stats`` functions, a rewrite of ``pearsonr``, and an exact computation of the Kolmogorov-Smirnov two-sample test - A new Cython API for bounded scalar-function root-finders in `scipy.optimize` - Substantial ``CSR`` and ``CSC`` sparse matrix indexing performance improvements - Added support for interpolation of rotations with continuous angular rate and acceleration in ``RotationSpline`` New features ============ `scipy.interpolate` improvements -------------------------------- A new class ``CubicHermiteSpline`` is introduced. It is a piecewise-cubic interpolator which matches observed values and first derivatives. Existing cubic interpolators ``CubicSpline``, ``PchipInterpolator`` and ``Akima1DInterpolator`` were made subclasses of ``CubicHermiteSpline``. `scipy.io` improvements ----------------------- For the Attribute-Relation File Format (ARFF) `scipy.io.arff.loadarff` now supports relational attributes. `scipy.io.mmread` can now parse Matrix Market format files with empty lines. `scipy.linalg` improvements --------------------------- Added wrappers for ``?syconv`` routines, which convert a symmetric matrix given by a triangular matrix factorization into two matrices and vice versa. `scipy.linalg.clarkson_woodruff_transform` now uses an algorithm that leverages sparsity. This may provide a 60-90 percent speedup for dense input matrices. Truly sparse input matrices should also benefit from the improved sketch algorithm, which now correctly runs in ``O(nnz(A))`` time. Added new functions to calculate symmetric Fiedler matrices and Fiedler companion matrices, named `scipy.linalg.fiedler` and `scipy.linalg.fiedler_companion`, respectively. These may be used for root finding. `scipy.ndimage` improvements ---------------------------- Gaussian filter performances may improve by an order of magnitude in some cases, thanks to removal of a dependence on ``np.polynomial``. This may impact `scipy.ndimage.gaussian_filter` for example. `scipy.optimize` improvements ----------------------------- The `scipy.optimize.brute` minimizer obtained a new keyword ``workers``, which can be used to parallelize computation. A Cython API for bounded scalar-function root-finders in `scipy.optimize` is available in a new module `scipy.optimize.cython_optimize` via ``cimport``. This API may be used with ``nogil`` and ``prange`` to loop over an array of function arguments to solve for an array of roots more quickly than with pure Python. ``'interior-point'`` is now the default method for ``linprog``, and ``'interior-point'`` now uses SuiteSparse for sparse problems when the required scikits (scikit-umfpack and scikit-sparse) are available. On benchmark problems (gh-10026), execution time reductions by factors of 2-3 were typical. Also, a new ``method='revised simplex'`` has been added. It is not as fast or robust as ``method='interior-point'``, but it is a faster, more robust, and equally accurate substitute for the legacy ``method='simplex'``. ``differential_evolution`` can now use a ``Bounds`` class to specify the bounds for the optimizing argument of a function. `scipy.optimize.dual_annealing` performance improvements related to vectorisation of some internal code. `scipy.signal` improvements --------------------------- Two additional methods of discretization are now supported by `scipy.signal.cont2discrete`: ``impulse`` and ``foh``. `scipy.signal.firls` now uses faster solvers `scipy.signal.detrend` now has a lower physical memory footprint in some cases, which may be leveraged using the new ``overwrite_data`` keyword argument `scipy.signal.firwin` ``pass_zero`` argument now accepts new string arguments that allow specification of the desired filter type: ``'bandpass'``, ``'lowpass'``, ``'highpass'``, and ``'bandstop'`` `scipy.signal.sosfilt` may have improved performance due to lower retention of the global interpreter lock (GIL) in algorithm `scipy.sparse` improvements --------------------------- A new keyword was added to ``csgraph.dijsktra`` that allows users to query the shortest path to ANY of the passed in indices, as opposed to the shortest path to EVERY passed index. `scipy.sparse.linalg.lsmr` performance has been improved by roughly 10 percent on large problems Improved performance and reduced physical memory footprint of the algorithm used by `scipy.sparse.linalg.lobpcg` ``CSR`` and ``CSC`` sparse matrix fancy indexing performance has been improved substantially `scipy.spatial` improvements ---------------------------- `scipy.spatial.ConvexHull` now has a ``good`` attribute that can be used alongsize the ``QGn`` Qhull options to determine which external facets of a convex hull are visible from an external query point. `scipy.spatial.cKDTree.query_ball_point` has been modernized to use some newer Cython features, including GIL handling and exception translation. An issue with ``return_sorted=True`` and scalar queries was fixed, and a new mode named ``return_length`` was added. ``return_length`` only computes the length of the returned indices list instead of allocating the array every time. `scipy.spatial.transform.RotationSpline` has been added to enable interpolation of rotations with continuous angular rates and acceleration `scipy.stats` improvements -------------------------- Added a new function to compute the Epps-Singleton test statistic, `scipy.stats.epps_singleton_2samp`, which can be applied to continuous and discrete distributions. New functions `scipy.stats.median_absolute_deviation` and `scipy.stats.gstd` (geometric standard deviation) were added. The `scipy.stats.combine_pvalues` method now supports ``pearson``, ``tippett`` and ``mudholkar_george`` pvalue combination methods. The `scipy.stats.ortho_group` and `scipy.stats.special_ortho_group` ``rvs(dim)`` functions' algorithms were updated from a ``O(dim^4)`` implementation to a ``O(dim^3)`` which gives large speed improvements for ``dim>100``. A rewrite of `scipy.stats.pearsonr` to use a more robust algorithm, provide meaningful exceptions and warnings on potentially pathological input, and fix at least five separate reported issues in the original implementation. Improved the precision of ``hypergeom.logcdf`` and ``hypergeom.logsf``. Added exact computation for Kolmogorov-Smirnov (KS) two-sample test, replacing the previously approximate computation for the two-sided test `stats.ks_2samp`. Also added a one-sided, two-sample KS test, and a keyword ``alternative`` to `stats.ks_2samp`. Backwards incompatible changes ============================== `scipy.interpolate` changes --------------------------- Functions from ``scipy.interpolate`` (``spleval``, ``spline``, ``splmake``, and ``spltopp``) and functions from ``scipy.misc`` (``bytescale``, ``fromimage``, ``imfilter``, ``imread``, ``imresize``, ``imrotate``, ``imsave``, ``imshow``, ``toimage``) have been removed. The former set has been deprecated since v0.19.0 and the latter has been deprecated since v1.0.0. Similarly, aliases from ``scipy.misc`` (``comb``, ``factorial``, ``factorial2``, ``factorialk``, ``logsumexp``, ``pade``, ``info``, ``source``, ``who``) which have been deprecated since v1.0.0 are removed. `SciPy documentation for v1.1.0 <https://docs.scipy.org/doc/scipy-1.1.0/reference/misc.html>`__ can be used to track the new import locations for the relocated functions. `scipy.linalg` changes ---------------------- For ``pinv``, ``pinv2``, and ``pinvh``, the default cutoff values are changed for consistency (see the docs for the actual values). `scipy.stats` changes --------------------- Previously, ``ks_2samp(data1, data2)`` would run a two-sided test and return the approximated p-value. The new signature, ``ks_2samp(data1, data2, alternative="two-sided", method="auto")``, still runs the two-sided test by default but returns the exact p-value for small samples and the approximated value for large samples. ``method="asymp"`` would be equivalent to the old version but ``auto`` is the better choice. Other changes ============= Our tutorial has been expanded with a new section on global optimizers There has been a rework of the ``stats.distributions`` tutorials. `scipy.optimize` now correctly sets the convergence flag of the result to ``CONVERR``, a convergence error, for bounded scalar-function root-finders if the maximum iterations has been exceeded, ``disp`` is false, and ``full_output`` is true. `scipy.optimize.curve_fit` no longer fails if ``xdata`` and ``ydata`` dtypes differ; they are both now automatically cast to ``float64``. `scipy.ndimage` functions including ``binary_erosion``, ``binary_closing``, and ``binary_dilation`` now require an integer value for the number of iterations, which alleviates a number of reported issues. Fixed normal approximation in case ``zero_method == "pratt"`` in `scipy.stats.wilcoxon`. Fixes for incorrect probabilities, broadcasting issues and thread-safety related to stats distributions setting member variables inside ``_argcheck()``. `scipy.optimize.newton` now correctly raises a ``RuntimeError``, when default arguments are used, in the case that a derivative of value zero is obtained, which is a special case of failing to converge. A draft toolchain roadmap is now available, laying out a compatibility plan including Python versions, C standards, and NumPy versions. Authors ======= * ananyashreyjain + * ApamNapat + * Scott Calabrese Barton + * Christoph Baumgarten * Peter Bell + * Jacob Blomgren + * Doctor Bob + * Mana Borwornpadungkitti + * Matthew Brett * Evgeni Burovski * CJ Carey * Vega Theil Carstensen + * Robert Cimrman * Forrest Collman + * Pietro Cottone + * David + * Idan David + * Christoph Deil * Dieter Werthmüller * Conner DiPaolo + * Dowon * Michael Dunphy + * Peter Andreas Entschev + * Gökçen Eraslan + * Johann Faouzi + * Yu Feng * Piotr Figiel + * Matthew H Flamm * Franz Forstmayr + * Christoph Gohlke * Richard Janis Goldschmidt + * Ralf Gommers * Lars Grueter * Sylvain Gubian * Matt Haberland * Yaroslav Halchenko * Charles Harris * Lindsey Hiltner * JakobStruye + * He Jia + * Jwink3101 + * Greg Kiar + * Julius Bier Kirkegaard * John Kirkham + * Thomas Kluyver * Vladimir Korolev + * Joseph Kuo + * Michael Lamparski + * Eric Larson * Denis Laxalde * Katrin Leinweber * Jesse Livezey * ludcila + * Dhruv Madeka + * Magnus + * Nikolay Mayorov * Mark Mikofski * Jarrod Millman * Markus Mohrhard + * Eric Moore * Andrew Nelson * Aki Nishimura + * OGordon100 + * Petar Mlinarić + * Stefan Peterson * Matti Picus + * Ilhan Polat * Aaron Pries + * Matteo Ravasi + * Tyler Reddy * Ashton Reimer + * Joscha Reimer * rfezzani + * Riadh + * Lucas Roberts * Heshy Roskes + * Mirko Scholz + * Taylor D. Scott + * Srikrishna Sekhar + * Kevin Sheppard + * Sourav Singh * skjerns + * Kai Striega * SyedSaifAliAlvi + * Gopi Manohar T + * Albert Thomas + * Timon + * Paul van Mulbregt * Jacob Vanderplas * Daniel Vargas + * Pauli Virtanen * VNMabus + * Stefan van der Walt * Warren Weckesser * Josh Wilson * Nate Yoder + * Roman Yurchak A total of 97 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 1.3.0 ----------------------- * `#1320 <https://github.com/scipy/scipy/issues/1320>`__: scipy.stats.distribution: problem with self.a, self.b if they... * `#2002 <https://github.com/scipy/scipy/issues/2002>`__: members set in scipy.stats.distributions.##._argcheck (Trac #1477) * `#2823 <https://github.com/scipy/scipy/issues/2823>`__: distribution methods add tmp * `#3220 <https://github.com/scipy/scipy/issues/3220>`__: Scipy.opimize.fmin_powell direc argument syntax unclear * `#3728 <https://github.com/scipy/scipy/issues/3728>`__: scipy.stats.pearsonr: possible bug with zero variance input * `#6805 <https://github.com/scipy/scipy/issues/6805>`__: error-in-scipy-wilcoxon-signed-rank-test-for-equal-series * `#6873 <https://github.com/scipy/scipy/issues/6873>`__: 'stats.boxcox' return all same values * `#7117 <https://github.com/scipy/scipy/issues/7117>`__: Warn users when using float32 input data to curve_fit and friends * `#7632 <https://github.com/scipy/scipy/issues/7632>`__: it's not possible to tell the \`optimize.least_squares\` solver... * `#7730 <https://github.com/scipy/scipy/issues/7730>`__: stats.pearsonr: Potential division by zero for dataset of length... * `#7933 <https://github.com/scipy/scipy/issues/7933>`__: stats.truncnorm fails when providing values outside truncation... * `#8033 <https://github.com/scipy/scipy/issues/8033>`__: Add standard filter types to firwin to set pass_zero intuitively... * `#8600 <https://github.com/scipy/scipy/issues/8600>`__: lfilter.c.src zfill has erroneous header * `#8692 <https://github.com/scipy/scipy/issues/8692>`__: Non-negative values of \`stats.hypergeom.logcdf\` * `#8734 <https://github.com/scipy/scipy/issues/8734>`__: Enable pip build isolation * `#8861 <https://github.com/scipy/scipy/issues/8861>`__: scipy.linalg.pinv gives wrong result while scipy.linalg.pinv2... * `#8915 <https://github.com/scipy/scipy/issues/8915>`__: need to fix macOS build against older numpy versions * `#8980 <https://github.com/scipy/scipy/issues/8980>`__: scipy.stats.pearsonr overflows with high values of x and y * `#9226 <https://github.com/scipy/scipy/issues/9226>`__: BUG: signal: SystemError: <built-in function _linear_filter>... * `#9254 <https://github.com/scipy/scipy/issues/9254>`__: BUG: root finders brentq, etc, flag says "converged" even if... * `#9308 <https://github.com/scipy/scipy/issues/9308>`__: Test failure - test_initial_constraints_as_canonical * `#9353 <https://github.com/scipy/scipy/issues/9353>`__: scipy.stats.pearsonr returns r=1 if r_num/r_den = inf * `#9359 <https://github.com/scipy/scipy/issues/9359>`__: Planck distribution is a geometric distribution * `#9381 <https://github.com/scipy/scipy/issues/9381>`__: linregress should warn user in 2x2 array case * `#9406 <https://github.com/scipy/scipy/issues/9406>`__: BUG: stats: In pearsonr, when r is nan, the p-value must also... * `#9437 <https://github.com/scipy/scipy/issues/9437>`__: Cannot create sparse matrix from size_t indexes * `#9518 <https://github.com/scipy/scipy/issues/9518>`__: Relational attributes in loadarff * `#9551 <https://github.com/scipy/scipy/issues/9551>`__: BUG: scipy.optimize.newton says the root of x^2+1 is zero. * `#9564 <https://github.com/scipy/scipy/issues/9564>`__: rv_sample accepts invalid input in scipy.stats * `#9565 <https://github.com/scipy/scipy/issues/9565>`__: improper handling of multidimensional input in stats.rv_sample * `#9581 <https://github.com/scipy/scipy/issues/9581>`__: Least-squares minimization fails silently when x and y data are... * `#9587 <https://github.com/scipy/scipy/issues/9587>`__: Outdated value for scipy.constants.au * `#9611 <https://github.com/scipy/scipy/issues/9611>`__: Overflow error with new way of p-value calculation in kendall... * `#9645 <https://github.com/scipy/scipy/issues/9645>`__: \`scipy.stats.mode\` crashes with variable length arrays (\`dtype=object\`) * `#9734 <https://github.com/scipy/scipy/issues/9734>`__: PendingDeprecationWarning for np.matrix with pytest * `#9786 <https://github.com/scipy/scipy/issues/9786>`__: stats.ks_2samp() misleading for small data sets. * `#9790 <https://github.com/scipy/scipy/issues/9790>`__: Excessive memory usage on detrend * `#9801 <https://github.com/scipy/scipy/issues/9801>`__: dual_annealing does not set the success attribute in OptimizeResult * `#9833 <https://github.com/scipy/scipy/issues/9833>`__: IntegrationWarning from mielke.stats() during build of html doc. * `#9835 <https://github.com/scipy/scipy/issues/9835>`__: scipy.signal.firls seems to be inefficient versus MATLAB firls * `#9864 <https://github.com/scipy/scipy/issues/9864>`__: Curve_fit does not check for empty input data if called with... * `#9869 <https://github.com/scipy/scipy/issues/9869>`__: scipy.ndimage.label: Minor documentation issue * `#9882 <https://github.com/scipy/scipy/issues/9882>`__: format at the wrong paranthesis in scipy.spatial.transform * `#9889 <https://github.com/scipy/scipy/issues/9889>`__: scipy.signal.find_peaks minor documentation issue * `#9890 <https://github.com/scipy/scipy/issues/9890>`__: Minkowski p-norm Issues in cKDTree For Values Other Than 2 Or... * `#9896 <https://github.com/scipy/scipy/issues/9896>`__: scipy.stats._argcheck sets (not just checks) values * `#9905 <https://github.com/scipy/scipy/issues/9905>`__: Memory error in ndimage.binary_erosion * `#9909 <https://github.com/scipy/scipy/issues/9909>`__: binary_dilation/erosion/closing crashes when iterations is float * `#9919 <https://github.com/scipy/scipy/issues/9919>`__: BUG: \`coo_matrix\` does not validate the \`shape\` argument. * `#9982 <https://github.com/scipy/scipy/issues/9982>`__: lsq_linear hangs/infinite loop with 'trf' method * `#10003 <https://github.com/scipy/scipy/issues/10003>`__: exponnorm.pdf returns NAN for small K * `#10011 <https://github.com/scipy/scipy/issues/10011>`__: Incorrect check for invalid rotation plane in scipy.ndimage.rotate * `#10024 <https://github.com/scipy/scipy/issues/10024>`__: Fails to build from git * `#10048 <https://github.com/scipy/scipy/issues/10048>`__: DOC: scipy.optimize.root_scalar * `#10068 <https://github.com/scipy/scipy/issues/10068>`__: DOC: scipy.interpolate.splev * `#10074 <https://github.com/scipy/scipy/issues/10074>`__: BUG: \`expm\` calculates the wrong coefficients in the backward... Pull requests for 1.3.0 ----------------------- * `#7827 <https://github.com/scipy/scipy/pull/7827>`__: ENH: sparse: overhaul of sparse matrix indexing * `#8431 <https://github.com/scipy/scipy/pull/8431>`__: ENH: Cython optimize zeros api * `#8743 <https://github.com/scipy/scipy/pull/8743>`__: DOC: Updated linalg.pinv, .pinv2, .pinvh docstrings * `#8744 <https://github.com/scipy/scipy/pull/8744>`__: DOC: added examples to remez docstring * `#9227 <https://github.com/scipy/scipy/pull/9227>`__: DOC: update description of "direc" parameter of "fmin_powell" * `#9263 <https://github.com/scipy/scipy/pull/9263>`__: ENH: optimize: added "revised simplex" for scipy.optimize.linprog * `#9325 <https://github.com/scipy/scipy/pull/9325>`__: DEP: Remove deprecated functions for 1.3.0 * `#9330 <https://github.com/scipy/scipy/pull/9330>`__: Add note on push and pull affine transformations * `#9423 <https://github.com/scipy/scipy/pull/9423>`__: DOC: Clearly state how 2x2 input arrays are handled in stats.linregress * `#9428 <https://github.com/scipy/scipy/pull/9428>`__: ENH: parallelised brute * `#9438 <https://github.com/scipy/scipy/pull/9438>`__: BUG: Initialize coo matrix with size_t indexes * `#9455 <https://github.com/scipy/scipy/pull/9455>`__: MAINT: Speed up get_(lapack,blas)_func * `#9465 <https://github.com/scipy/scipy/pull/9465>`__: MAINT: Clean up optimize.zeros C solvers interfaces/code. * `#9477 <https://github.com/scipy/scipy/pull/9477>`__: DOC: linalg: fix lstsq docstring on residues shape * `#9478 <https://github.com/scipy/scipy/pull/9478>`__: DOC: Add docstring examples for rosen functions * `#9479 <https://github.com/scipy/scipy/pull/9479>`__: DOC: Add docstring example for ai_zeros and bi_zeros * `#9480 <https://github.com/scipy/scipy/pull/9480>`__: MAINT: linalg: lstsq clean up * `#9489 <https://github.com/scipy/scipy/pull/9489>`__: DOC: roadmap update for changes over the last year. * `#9492 <https://github.com/scipy/scipy/pull/9492>`__: MAINT: stats: Improve implementation of chi2 ppf method. * `#9497 <https://github.com/scipy/scipy/pull/9497>`__: DOC: Improve docstrings sparse.linalg.isolve * `#9499 <https://github.com/scipy/scipy/pull/9499>`__: DOC: Replace "Scipy" with "SciPy" in the .rst doc files for consistency. * `#9500 <https://github.com/scipy/scipy/pull/9500>`__: DOC: Document the toolchain and its roadmap. * `#9505 <https://github.com/scipy/scipy/pull/9505>`__: DOC: specify which definition of skewness is used * `#9511 <https://github.com/scipy/scipy/pull/9511>`__: DEP: interpolate: remove deprecated interpolate_wrapper * `#9517 <https://github.com/scipy/scipy/pull/9517>`__: BUG: improve error handling in stats.iqr * `#9522 <https://github.com/scipy/scipy/pull/9522>`__: ENH: Add Fiedler and fiedler companion to special matrices * `#9526 <https://github.com/scipy/scipy/pull/9526>`__: TST: relax precision requirements in signal.correlate tests * `#9529 <https://github.com/scipy/scipy/pull/9529>`__: DOC: fix missing random seed in optimize.newton example * `#9533 <https://github.com/scipy/scipy/pull/9533>`__: MAINT: Use list comprehension when possible * `#9537 <https://github.com/scipy/scipy/pull/9537>`__: DOC: add a "big picture" roadmap * `#9538 <https://github.com/scipy/scipy/pull/9538>`__: DOC: Replace "Numpy" with "NumPy" in .py, .rst and .txt doc files... * `#9539 <https://github.com/scipy/scipy/pull/9539>`__: ENH: add two-sample test (Epps-Singleton) to scipy.stats * `#9559 <https://github.com/scipy/scipy/pull/9559>`__: DOC: add section on global optimizers to tutorial * `#9561 <https://github.com/scipy/scipy/pull/9561>`__: ENH: remove noprefix.h, change code appropriately * `#9562 <https://github.com/scipy/scipy/pull/9562>`__: MAINT: stats: Rewrite pearsonr. * `#9563 <https://github.com/scipy/scipy/pull/9563>`__: BUG: Minor bug fix Callback in linprog(method='simplex') * `#9568 <https://github.com/scipy/scipy/pull/9568>`__: MAINT: raise runtime error for newton with zeroder if disp true,... * `#9570 <https://github.com/scipy/scipy/pull/9570>`__: Correct docstring in show_options in optimize. Fixes #9407 * `#9573 <https://github.com/scipy/scipy/pull/9573>`__: BUG fixes range of pk variable pre-check * `#9577 <https://github.com/scipy/scipy/pull/9577>`__: TST: fix minor issue in a signal.stft test. * `#9580 <https://github.com/scipy/scipy/pull/9580>`__: Included blank line before list - Fixes #8658 * `#9582 <https://github.com/scipy/scipy/pull/9582>`__: MAINT: drop Python 2.7 and 3.4 * `#9588 <https://github.com/scipy/scipy/pull/9588>`__: MAINT: update \`constants.astronomical_unit\` to new 2012 value. * `#9592 <https://github.com/scipy/scipy/pull/9592>`__: TST: Add 32-bit testing to CI * `#9593 <https://github.com/scipy/scipy/pull/9593>`__: DOC: Replace cumulative density with cumulative distribution * `#9596 <https://github.com/scipy/scipy/pull/9596>`__: TST: remove VC 9.0 from Azure CI * `#9599 <https://github.com/scipy/scipy/pull/9599>`__: Hyperlink DOI to preferred resolver * `#9601 <https://github.com/scipy/scipy/pull/9601>`__: DEV: try to limit GC memory use on PyPy * `#9603 <https://github.com/scipy/scipy/pull/9603>`__: MAINT: improve logcdf and logsf of hypergeometric distribution * `#9605 <https://github.com/scipy/scipy/pull/9605>`__: Reference to pylops in LinearOperator notes and ARPACK example * `#9617 <https://github.com/scipy/scipy/pull/9617>`__: TST: reduce max memory usage for sparse.linalg.lgmres test * `#9619 <https://github.com/scipy/scipy/pull/9619>`__: FIX: Sparse matrix addition/subtraction eliminates explicit zeros * `#9621 <https://github.com/scipy/scipy/pull/9621>`__: bugfix in rv_sample in scipy.stats * `#9622 <https://github.com/scipy/scipy/pull/9622>`__: MAINT: Raise error in directed_hausdorff distance * `#9623 <https://github.com/scipy/scipy/pull/9623>`__: DOC: Build docs with warnings as errors * `#9625 <https://github.com/scipy/scipy/pull/9625>`__: Return the number of calls to 'hessp' (not just 'hess') in trust... * `#9627 <https://github.com/scipy/scipy/pull/9627>`__: BUG: ignore empty lines in mmio * `#9637 <https://github.com/scipy/scipy/pull/9637>`__: Function to calculate the MAD of an array * `#9646 <https://github.com/scipy/scipy/pull/9646>`__: BUG: stats: mode for objects w/ndim > 1 * `#9648 <https://github.com/scipy/scipy/pull/9648>`__: Add \`stats.contingency\` to refguide-check * `#9650 <https://github.com/scipy/scipy/pull/9650>`__: ENH: many lobpcg() algorithm improvements * `#9652 <https://github.com/scipy/scipy/pull/9652>`__: Move misc.doccer to _lib.doccer * `#9660 <https://github.com/scipy/scipy/pull/9660>`__: ENH: add pearson, tippett, and mudholkar-george to combine_pvalues * `#9661 <https://github.com/scipy/scipy/pull/9661>`__: BUG: Fix ksone right-hand endpoint, documentation and tests. * `#9664 <https://github.com/scipy/scipy/pull/9664>`__: ENH: adding multi-target dijsktra performance enhancement * `#9670 <https://github.com/scipy/scipy/pull/9670>`__: MAINT: link planck and geometric distribution in scipy.stats * `#9676 <https://github.com/scipy/scipy/pull/9676>`__: ENH: optimize: change default linprog method to interior-point * `#9685 <https://github.com/scipy/scipy/pull/9685>`__: Added reference to ndimage.filters.median_filter * `#9705 <https://github.com/scipy/scipy/pull/9705>`__: Fix coefficients in expm helper function * `#9711 <https://github.com/scipy/scipy/pull/9711>`__: Release the GIL during sosfilt processing for simple types * `#9721 <https://github.com/scipy/scipy/pull/9721>`__: ENH: Convexhull visiblefacets * `#9723 <https://github.com/scipy/scipy/pull/9723>`__: BLD: Modify rv_generic._construct_doc to print out failing distribution... * `#9726 <https://github.com/scipy/scipy/pull/9726>`__: BUG: Fix small issues with \`signal.lfilter' * `#9729 <https://github.com/scipy/scipy/pull/9729>`__: BUG: Typecheck iterations for binary image operations * `#9730 <https://github.com/scipy/scipy/pull/9730>`__: ENH: reduce sizeof(NI_WatershedElement) by 20% * `#9731 <https://github.com/scipy/scipy/pull/9731>`__: ENH: remove suspicious sequence of type castings * `#9739 <https://github.com/scipy/scipy/pull/9739>`__: BUG: qr_updates fails if u is exactly in span Q * `#9749 <https://github.com/scipy/scipy/pull/9749>`__: BUG: MapWrapper.__exit__ should terminate * `#9753 <https://github.com/scipy/scipy/pull/9753>`__: ENH: Added exact computation for Kolmogorov-Smirnov two-sample... * `#9755 <https://github.com/scipy/scipy/pull/9755>`__: DOC: Added example for signal.impulse, copied from impulse2 * `#9756 <https://github.com/scipy/scipy/pull/9756>`__: DOC: Added docstring example for iirdesign * `#9757 <https://github.com/scipy/scipy/pull/9757>`__: DOC: Added examples for step functions * `#9759 <https://github.com/scipy/scipy/pull/9759>`__: ENH: Allow pass_zero to act like btype * `#9760 <https://github.com/scipy/scipy/pull/9760>`__: DOC: Added docstring for lp2bs * `#9761 <https://github.com/scipy/scipy/pull/9761>`__: DOC: Added docstring and example for lp2bp * `#9764 <https://github.com/scipy/scipy/pull/9764>`__: BUG: Catch internal warnings for matrix * `#9766 <https://github.com/scipy/scipy/pull/9766>`__: ENH: Speed up _gaussian_kernel1d by removing dependence on np.polynomial * `#9769 <https://github.com/scipy/scipy/pull/9769>`__: BUG: Fix Cubic Spline Read Only issues * `#9773 <https://github.com/scipy/scipy/pull/9773>`__: DOC: Several docstrings * `#9774 <https://github.com/scipy/scipy/pull/9774>`__: TST: bump Azure CI OpenBLAS version to match wheels * `#9775 <https://github.com/scipy/scipy/pull/9775>`__: DOC: Improve clarity of cov_x documentation for scipy.optimize.leastsq * `#9779 <https://github.com/scipy/scipy/pull/9779>`__: ENH: dual_annealing vectorise visit_fn * `#9788 <https://github.com/scipy/scipy/pull/9788>`__: TST, BUG: f2py-related issues with NumPy < 1.14.0 * `#9791 <https://github.com/scipy/scipy/pull/9791>`__: BUG: fix amax constraint not enforced in scalar_search_wolfe2 * `#9792 <https://github.com/scipy/scipy/pull/9792>`__: ENH: Allow inplace copying in place in "detrend" function * `#9795 <https://github.com/scipy/scipy/pull/9795>`__: DOC: Fix/update docstring for dstn and dst * `#9796 <https://github.com/scipy/scipy/pull/9796>`__: MAINT: Allow None tolerances in least_squares * `#9798 <https://github.com/scipy/scipy/pull/9798>`__: BUG: fixes abort trap 6 error in scipy issue 9785 in unit tests * `#9807 <https://github.com/scipy/scipy/pull/9807>`__: MAINT: improve doc and add alternative keyword to wilcoxon in... * `#9808 <https://github.com/scipy/scipy/pull/9808>`__: Fix PPoly integrate and test for CubicSpline * `#9810 <https://github.com/scipy/scipy/pull/9810>`__: ENH: Add the geometric standard deviation function * `#9811 <https://github.com/scipy/scipy/pull/9811>`__: MAINT: remove invalid derphi default None value in scalar_search_wolfe2 * `#9813 <https://github.com/scipy/scipy/pull/9813>`__: Adapt hamming distance in C to support weights * `#9817 <https://github.com/scipy/scipy/pull/9817>`__: DOC: Copy solver description to solver modules * `#9829 <https://github.com/scipy/scipy/pull/9829>`__: ENH: Add FOH and equivalent impulse response discretizations... * `#9831 <https://github.com/scipy/scipy/pull/9831>`__: ENH: Implement RotationSpline * `#9834 <https://github.com/scipy/scipy/pull/9834>`__: DOC: Change mielke distribution default parameters to ensure... * `#9838 <https://github.com/scipy/scipy/pull/9838>`__: ENH: Use faster solvers for firls * `#9854 <https://github.com/scipy/scipy/pull/9854>`__: ENH: loadarff now supports relational attributes. * `#9856 <https://github.com/scipy/scipy/pull/9856>`__: integrate.bvp - improve handling of nonlinear boundary conditions * `#9862 <https://github.com/scipy/scipy/pull/9862>`__: TST: reduce Appveyor CI load * `#9874 <https://github.com/scipy/scipy/pull/9874>`__: DOC: Update requirements in release notes * `#9883 <https://github.com/scipy/scipy/pull/9883>`__: BUG: fixed parenthesis in spatial.rotation * `#9884 <https://github.com/scipy/scipy/pull/9884>`__: ENH: Use Sparsity in Clarkson-Woodruff Sketch * `#9888 <https://github.com/scipy/scipy/pull/9888>`__: MAINT: Replace NumPy aliased functions * `#9892 <https://github.com/scipy/scipy/pull/9892>`__: BUG: Fix 9890 query_ball_point returns wrong result when p is... * `#9893 <https://github.com/scipy/scipy/pull/9893>`__: BUG: curve_fit doesn't check for empty input if called with bounds * `#9894 <https://github.com/scipy/scipy/pull/9894>`__: scipy.signal.find_peaks documentation error * `#9898 <https://github.com/scipy/scipy/pull/9898>`__: BUG: Set success attribute in OptimizeResult. See #9801 * `#9900 <https://github.com/scipy/scipy/pull/9900>`__: BUG: Restrict rv_generic._argcheck() and its overrides from setting... * `#9906 <https://github.com/scipy/scipy/pull/9906>`__: fixed a bug in kde logpdf * `#9911 <https://github.com/scipy/scipy/pull/9911>`__: DOC: replace example for "np.select" with the one from numpy... * `#9912 <https://github.com/scipy/scipy/pull/9912>`__: BF(DOC): point to numpy.select instead of plain (python) .select * `#9914 <https://github.com/scipy/scipy/pull/9914>`__: DOC: change ValueError message in _validate_pad of signaltools. * `#9915 <https://github.com/scipy/scipy/pull/9915>`__: cKDTree query_ball_point improvements * `#9918 <https://github.com/scipy/scipy/pull/9918>`__: Update ckdtree.pyx with boxsize argument in docstring * `#9920 <https://github.com/scipy/scipy/pull/9920>`__: BUG: sparse: Validate explicit shape if given with dense argument... * `#9924 <https://github.com/scipy/scipy/pull/9924>`__: BLD: add back pyproject.toml * `#9931 <https://github.com/scipy/scipy/pull/9931>`__: Fix empty constraint * `#9935 <https://github.com/scipy/scipy/pull/9935>`__: DOC: fix references for stats.f_oneway * `#9936 <https://github.com/scipy/scipy/pull/9936>`__: Revert gh-9619: "FIX: Sparse matrix addition/subtraction eliminates... * `#9937 <https://github.com/scipy/scipy/pull/9937>`__: MAINT: fix PEP8 issues and update to pycodestyle 2.5.0 * `#9939 <https://github.com/scipy/scipy/pull/9939>`__: DOC: correct \`structure\` description in \`ndimage.label\` docstring * `#9940 <https://github.com/scipy/scipy/pull/9940>`__: MAINT: remove extraneous distutils copies * `#9945 <https://github.com/scipy/scipy/pull/9945>`__: ENH: differential_evolution can use Bounds object * `#9949 <https://github.com/scipy/scipy/pull/9949>`__: Added 'std' to add doctstrings since it is a \`known_stats\`... * `#9953 <https://github.com/scipy/scipy/pull/9953>`__: DOC: Documentation cleanup for stats tutorials. * `#9962 <https://github.com/scipy/scipy/pull/9962>`__: __repr__ for Bounds * `#9971 <https://github.com/scipy/scipy/pull/9971>`__: ENH: Improve performance of lsmr * `#9987 <https://github.com/scipy/scipy/pull/9987>`__: CI: pin Sphinx version to 1.8.5 * `#9990 <https://github.com/scipy/scipy/pull/9990>`__: ENH: constraint violation * `#9991 <https://github.com/scipy/scipy/pull/9991>`__: BUG: Avoid inplace modification of input array in newton * `#9995 <https://github.com/scipy/scipy/pull/9995>`__: MAINT: sparse.csgraph: Add cdef to stop build warning. * `#9996 <https://github.com/scipy/scipy/pull/9996>`__: BUG: Make minimize_quadratic_1d work with infinite bounds correctly * `#10004 <https://github.com/scipy/scipy/pull/10004>`__: BUG: Fix unbound local error in linprog - simplex. * `#10007 <https://github.com/scipy/scipy/pull/10007>`__: BLD: fix Python 3.7 build with build isolation * `#10009 <https://github.com/scipy/scipy/pull/10009>`__: BUG: Make sure that _binary_erosion only accepts an integer number... * `#10016 <https://github.com/scipy/scipy/pull/10016>`__: Update link to airspeed-velocity * `#10017 <https://github.com/scipy/scipy/pull/10017>`__: DOC: Update \`interpolate.LSQSphereBivariateSpline\` to include... * `#10018 <https://github.com/scipy/scipy/pull/10018>`__: MAINT: special: Fix a few warnings that occur when compiling... * `#10019 <https://github.com/scipy/scipy/pull/10019>`__: TST: Azure summarizes test failures * `#10021 <https://github.com/scipy/scipy/pull/10021>`__: ENH: Introduce CubicHermiteSpline * `#10022 <https://github.com/scipy/scipy/pull/10022>`__: BENCH: Increase cython version in asv to fix benchmark builds * `#10023 <https://github.com/scipy/scipy/pull/10023>`__: BUG: Avoid exponnorm producing nan for small K values. * `#10025 <https://github.com/scipy/scipy/pull/10025>`__: BUG: optimize: tweaked linprog status 4 error message * `#10026 <https://github.com/scipy/scipy/pull/10026>`__: ENH: optimize: use SuiteSparse in linprog interior-point when... * `#10027 <https://github.com/scipy/scipy/pull/10027>`__: MAINT: cluster: clean up the use of malloc() in the function... * `#10028 <https://github.com/scipy/scipy/pull/10028>`__: Fix rotate invalid plane check * `#10040 <https://github.com/scipy/scipy/pull/10040>`__: MAINT: fix pratt method of wilcox test in scipy.stats * `#10041 <https://github.com/scipy/scipy/pull/10041>`__: MAINT: special: Fix a warning generated when building the AMOS... * `#10044 <https://github.com/scipy/scipy/pull/10044>`__: DOC: fix up spatial.transform.Rotation docstrings * `#10047 <https://github.com/scipy/scipy/pull/10047>`__: MAINT: interpolate: Fix a few build warnings. * `#10051 <https://github.com/scipy/scipy/pull/10051>`__: Add project_urls to setup * `#10052 <https://github.com/scipy/scipy/pull/10052>`__: don't set flag to "converged" if max iter exceeded * `#10054 <https://github.com/scipy/scipy/pull/10054>`__: MAINT: signal: Fix a few build warnings and modernize some C... * `#10056 <https://github.com/scipy/scipy/pull/10056>`__: BUG: Ensure factorial is not too large in kendaltau * `#10058 <https://github.com/scipy/scipy/pull/10058>`__: Small speedup in samping from ortho and special_ortho groups * `#10059 <https://github.com/scipy/scipy/pull/10059>`__: BUG: optimize: fix #10038 by increasing tol * `#10061 <https://github.com/scipy/scipy/pull/10061>`__: BLD: DOC: make building docs easier by parsing python version. * `#10064 <https://github.com/scipy/scipy/pull/10064>`__: ENH: Significant speedup for ortho and special ortho group * `#10065 <https://github.com/scipy/scipy/pull/10065>`__: DOC: Reword parameter descriptions in \`optimize.root_scalar\` * `#10066 <https://github.com/scipy/scipy/pull/10066>`__: BUG: signal: Fix error raised by savgol_coeffs when deriv > polyorder. * `#10067 <https://github.com/scipy/scipy/pull/10067>`__: MAINT: Fix the cutoff value inconsistency for pinv2 and pinvh * `#10072 <https://github.com/scipy/scipy/pull/10072>`__: BUG: stats: Fix boxcox_llf to avoid loss of precision. * `#10075 <https://github.com/scipy/scipy/pull/10075>`__: ENH: Add wrappers for ?syconv routines * `#10076 <https://github.com/scipy/scipy/pull/10076>`__: BUG: optimize: fix curve_fit for mixed float32/float64 input * `#10077 <https://github.com/scipy/scipy/pull/10077>`__: DOC: Replace undefined \`k\` in \`interpolate.splev\` docstring * `#10079 <https://github.com/scipy/scipy/pull/10079>`__: DOC: Fixed typo, rearranged some doc of stats.morestats.wilcoxon. * `#10080 <https://github.com/scipy/scipy/pull/10080>`__: TST: install scikit-sparse for full TravisCI tests * `#10083 <https://github.com/scipy/scipy/pull/10083>`__: Clean \`\`_clean_inputs\`\` in optimize.linprog * `#10088 <https://github.com/scipy/scipy/pull/10088>`__: ENH: optimize: linprog test CHOLMOD/UMFPACK solvers when available * `#10090 <https://github.com/scipy/scipy/pull/10090>`__: MAINT: Fix CubicSplinerInterpolator for pandas * `#10091 <https://github.com/scipy/scipy/pull/10091>`__: MAINT: improve logcdf and logsf of hypergeometric distribution * `#10095 <https://github.com/scipy/scipy/pull/10095>`__: MAINT: Clean \`\`_clean_inputs\`\` in linprog Checksums ========= MD5 ~~~ 5a71a217fa4ff372097f501daf816f3b scipy-1.3.0rc1-cp35-cp35m-macosx_10_6_intel.macosx_10_9_int el.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl c154ca8eee9ebafe04575b316e41ed85 scipy-1.3.0rc1-cp35-cp35m-manylinux1_i686.whl 36a91fa4ae6eeceeb79bf97b9bd013eb scipy-1.3.0rc1-cp35-cp35m-manylinux1_x86_64.whl f1f4259b373332d6edc6bef123b0dc7c scipy-1.3.0rc1-cp35-cp35m-win32.whl c81d78bed8e2176cf0168785b7e1b692 scipy-1.3.0rc1-cp35-cp35m-win_amd64.whl c43dd24f349c9d37a6c996e7c0674141 scipy-1.3.0rc1-cp36-cp36m-macosx_10_6_intel.macosx_10_9_int el.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl 8188210e0fd710f4544f314306b76313 scipy-1.3.0rc1-cp36-cp36m-manylinux1_i686.whl 0cf317ee185a8f5736b479d1c8b5f415 scipy-1.3.0rc1-cp36-cp36m-manylinux1_x86_64.whl e46e5b38288d79321d8d6ffa15a8f54e scipy-1.3.0rc1-cp36-cp36m-win32.whl 85a79e9be408de72056c6efc1eef7d46 scipy-1.3.0rc1-cp36-cp36m-win_amd64.whl 2436169658f74e03b4037142e51a8f86 scipy-1.3.0rc1-cp37-cp37m-macosx_10_6_intel.macosx_10_9_int el.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl 1b9e2caa5994ee227b4ad8e46b45ad7e scipy-1.3.0rc1-cp37-cp37m-manylinux1_i686.whl 05a51b40471abdf4e9020183ad449bf2 scipy-1.3.0rc1-cp37-cp37m-manylinux1_x86_64.whl debecbc0e54fe4e737971b0a6d9f24f5 scipy-1.3.0rc1-cp37-cp37m-win32.whl 79c725144fa59566d8ebd3bf556533aa scipy-1.3.0rc1-cp37-cp37m-win_amd64.whl 0b9fa3583bcf2c8190b277cddb287132 scipy-1.3.0rc1.tar.gz d22a40e138ecd6bb26990e22d4a1ac1b scipy-1.3.0rc1.tar.xz 9cc12f26980587900befabafaac2078b scipy-1.3.0rc1.zip SHA256 ~~~~~~ 3491e5453acec48ff8bc1e96980a9ca225bf653eb8e2fad2efe44ca54fd61230 scipy-1.3.0rc1-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 b04fed432d0d2b7aa52fb83c87390f22e34407baa404385e6c804c6d2f9fe3dc scipy-1.3.0rc1-cp35-cp35m-manylinux1_i686.whl 71c236d8b036caa84a018b056c6ced101bcb3efb160fab18957daf5a41c7319c scipy-1.3.0rc1-cp35-cp35m-manylinux1_x86_64.whl 6fa6a341ab6920f9233ce5da16572e3e403540f90c17269e27a7a7451e05d40e scipy-1.3.0rc1-cp35-cp35m-win32.whl 7ec09797276d26c74234c056415234a7168e536011767af984b1700410806699 scipy-1.3.0rc1-cp35-cp35m-win_amd64.whl 55fcdd1ea9bb3d5461477391d924e24c56c8fa3cb3aba98c2ee2c47e3ccd6ce2 scipy-1.3.0rc1-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 b149d330d0a8219b68d2839cc49a37df163b0ec0b56194f8f0aa6878c1e7c2a4 scipy-1.3.0rc1-cp36-cp36m-manylinux1_i686.whl 9fed021210077c2e183f621d84bef428762b0b226f8f6da2b03a7d93628e3089 scipy-1.3.0rc1-cp36-cp36m-manylinux1_x86_64.whl 5c9c9a47e914fbf8edc9a1da1e10a9f5204b3dfc89c93b721b658290884bfe45 scipy-1.3.0rc1-cp36-cp36m-win32.whl 09b2c3e099b6274b142e7e05132e79efbbe4daa9dd593a226a3bc9820adf966a scipy-1.3.0rc1-cp36-cp36m-win_amd64.whl 4e93edc6d4c1296ac39ae4be2e8d9336a37a3e5c6e104801a288db0f18d5dbd1 scipy-1.3.0rc1-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 1edafef721c859848b8575e7313fba062903f3c256661304f488b96fff4f759d scipy-1.3.0rc1-cp37-cp37m-manylinux1_i686.whl 2fe186fff442d3f54f8e6950e809c571ea29db8333ed30608c4074a843d5cdf1 scipy-1.3.0rc1-cp37-cp37m-manylinux1_x86_64.whl 7c59ec7d5148538978da6c66059c9e3240ae9cf17b803b15354fffc8d3320961 scipy-1.3.0rc1-cp37-cp37m-win32.whl 2ab6d6f940b6b09cbee6d7cb2de5914a50f459cbc612b4328b033f67452fd1d6 scipy-1.3.0rc1-cp37-cp37m-win_amd64.whl d09e6fae7434aa9e1422d95bbee28f0b66ba97ab770fec24f31c75a873823cd6 scipy-1.3.0rc1.tar.gz ba49645a693f6e70e690cf6e2175865b7bf0182cf59bdce872968f47546f4269 scipy-1.3.0rc1.tar.xz f1cdb4651f3d150f5c145dc930a627f09aa2afc5275c6c5da97a9a5df274c531 scipy-1.3.0rc1.zip -----BEGIN PGP SIGNATURE----- Version: GnuPG v1 iQIcBAEBAgAGBQJcw54gAAoJELD/41ZX0J71zbEQAKaapoUb0gbLvljeLL+FZmaZ Ha8OecYf7Fsdgj9kiGk2kKgrvz5nkoRyJDpxi4BOzPua+HBUhECoH3DBL5kA6sbv VsMAhTb5HFPiKmC78LHEUNSY1fPJrdK7s99pIgedjQFR5diODHqqtB54awbRsTOs Vdq45I7JoCd+DUMqQWIA3TZyjrZzw2V1KBFS8mHFdcop71Q1RqRNf3rVw7Rpydob uegbae42cJ2Hej4+viU8hsCM+JIgkCuZaQEN2wp4W9pmHsDCzJcoyQjulTjZQAeG W3L/F5O1p9A9nHIPk+wvS3D2ageKOhYmVSgB6dznXnFRsjwIKH4O4k0TCzYAamsd HvcKnncGAzc+95o2k+v46575O3pBPRYCmOKz6LlFfFGNr/PWkxPYuG49nGIwQZU+ /w0RYvu2NIPyd05gQQfwyEAATmwbYQfCelbQtHtPehDrtwMINZF4ZCqVg8D7d2ns c2mUUC72Iq62R17CdQOjp/zkvU4Xo6KGm0TPQY7xuJwXOaiM5cJ49iY+/Z6eBfBM JS0MoIZ8PRzhzQ6gKTPt4exil75ybNpCeL/Ny/LY5dgkfaOdTuplmxwxgngxaECG W4SaGw4P0SiATwyz7hMprv/Xkq59iK6IsF6Ki7uxPuMps+nbYhvVIUXqh8Y5iVxx piFfF2ct8rgxkVm3qBmN =KK2K -----END PGP SIGNATURE-----

2 years, 7 months

NumPy Community Meeting on May 1/ 2019

by Tyler Reddy

Hi, There will be a NumPy community meeting at 12 pm Pacific Time on May 1/ 2019. Anyone may join & edit the work in progress meeting notes available here: https://hackmd.io/MzSVnXjiR7ykM6ocNKAOxg?view Best wishes, Tyler

2 years, 7 months

ANN: NumPy and SciPy accepted in Summer of Docs, technical writer exploration phase open

by Ralf Gommers

Hi all, Good news: NumPy and SciPy both were accepted as participating organizations for Google Summer of Docs! This could potentially be very interesting and impactful. If you have any tech writer friends that could be interested or that may have friends, colleagues or a community that may be: please point them to the announcement! Links for program details below. Our ideas pages are at: https://github.com/numfocus/gsod/blob/master/2019/NumPy_ideas_list.md https://github.com/numfocus/gsod/blob/master/2019/SciPy_ideas_list.md Cheers, Ralf ---------- Forwarded message --------- From: 'Andrew Chen' via Season of Docs - Announce < season-of-docs-announce(a)googlegroups.com> Date: Tue, Apr 30, 2019 at 2:30 PM Subject: [Season of Docs - Announcements] Participating organizations announced and technical writer exploration phase open To: Season of Docs - Announce <season-of-docs-announce(a)googlegroups.com> Thank you for your enthusiasm and support for Season of Docs. We received nearly 200 organization applications! There were many high quality applications, so the selection of organizations was not easy. The list of 50 participating open source organizations for Season of Docs 2019 is now available at https://developers.google.com/season-of-docs/docs/participants/. Are you a technical writer interested in taking part in Season of Docs? Please explore the list of participating organizations and their project ideas. If any of the projects interest you, you can contact the open source organization now to start fleshing out your project proposal. You can explore more than one project, and when the technical writer application phase opens you can submit more than one project proposal, but only one proposal will be accepted. The exploration phase runs from April 30 to May 28. Take a look at the technical writer guidelines <https://developers.google.com/season-of-docs/docs/tech-writer-guide#tech-wr…> for this phase. You can submit your project proposals to Season of Docs when the technical writer application phase opens on May 29. See the full timeline <https://developers.google.com/season-of-docs/docs/timeline> on the Season of Docs website. We hope you enjoy exploring the available open source projects and choosing the project you’d like to work with! If you have any questions about the program, please email the Season of Docs team at season-of-docs-support(a)googlegroups.com. -- You received this message because you are subscribed to the Google Groups "Season of Docs - Announce" group. To unsubscribe from this group and stop receiving emails from it, send an email to season-of-docs-announce+unsubscribe(a)googlegroups.com. To view this discussion on the web visit https://groups.google.com/d/msgid/season-of-docs-announce/aac7c948-d19f-48d… <https://groups.google.com/d/msgid/season-of-docs-announce/aac7c948-d19f-48d…> . For more options, visit https://groups.google.com/d/optout.

2 years, 7 months

how much does binary size matter?

by Ralf Gommers

Hi all, In https://github.com/numpy/numpy/pull/13207 a discussion started about the tradeoff between performance gain for one function vs increasing the size of a NumPy build by a couple of percent. We also discussed that in the community call on Wednesday and concluded that it may be useful to ask here for some more feedback. Beyond disk/memory usage and download bandwidth, are there specific problems that people are struggling with regarding size of numpy binaries? Right now a wheel is 16 MB. If we increase that by 10%/50%/100% - are we causing a real problem for someone? Thanks, Ralf

2 years, 7 months

f2py is not recognizing Absoft Fortran Compiler

by Samuel Dupree

I'm attempting to build an interface between a Fortran application I have and Python. The problem I'm having is that f2py is not recognizing my Absoft Fortran compiler. Details follow. I'm running on a iMacPro (2017) under Mac OS X Mojave (ver. 10.14.4). The version of the Absoft Compiler I'm running is 19.0.0. I'm using the Anaconda distribution of Python 3, version 3.6.8, where the version of NumPy used is ver. 1.16.3. The commands I'm using to build the Python interface function and there results are: f2py -m DIVA_EphGen_f77 -h DIVA_EphGen_f77.pyf DIVA_EphGen_f77.f --overwrite-signature (base) Samuels-Mac-Pro:2nd_ODE_auto_deriv_cleanup_Rev-07 user$ ./BuildMe_Part-1.sh Reading fortran codes... Reading file 'DIVA_EphGen_f77.f' (format:fix,strict) Post-processing... Block: DIVA_EphGen_f77 Block: diva_ephgen_f77 Post-processing (stage 2)... Saving signatures to file "./DIVA_EphGen_f77.pyf" (base) Samuels-Mac-Pro:2nd_ODE_auto_deriv_cleanup_Rev-07 user$ f2py -c --fcompiler=absoft DIVA_EphGen_f77.pyf DIVA_EphGen_f77.f \ --f77exec=/Applications/Absoft19.0/bin/af77 \ -L/Users/user/2nd_ODE_auto_deriv_cleanup_Rev-07/ -lDIVA_EphGen \ -L/Volumes/Development/AstrodynamicsLib/MacOSX.NAIF.Fortran.v65/toolkit-64-bit/lib/ -lspicelib \ -L/Volumes/Development/AstrodynamicsLib/MacOSX.NAIF.Fortran.v65/toolkit-64-bit/lib/ -lsupport \ -L/Application/Absoft19.0/lib64/ -lblas \ -L/Application/Absoft19.0/lib64/ -lAbsoftlapack \ --verbose (base) Samuels-Mac-Pro:2nd_ODE_auto_deriv_cleanup_Rev-07 user$ ./BuildMe_Part-2.sh running build running config_cc unifing config_cc, config, build_clib, build_ext, build commands --compiler options running config_fc unifing config_fc, config, build_clib, build_ext, build commands --fcompiler options running build_src build_src building extension "DIVA_EphGen_f77" sources creating /var/folders/65/7cxpqns144q6wx5lyb7zcyh80000gq/T/tmpkv4x9yg7/src.macosx-10.9-x86_64-3.6 f2py options: [] f2py: DIVA_EphGen_f77.pyf Reading fortran codes... Reading file 'DIVA_EphGen_f77.pyf' (format:free) Post-processing... Block: DIVA_EphGen_f77 Block: diva_ephgen_f77 Post-processing (stage 2)... Building modules... Building module "DIVA_EphGen_f77"... Constructing wrapper function "diva_ephgen_f77"... diva_ephgen_f77(cmdline,scale_factor) Wrote C/API module "DIVA_EphGen_f77" to file "/var/folders/65/7cxpqns144q6wx5lyb7zcyh80000gq/T/tmpkv4x9yg7/src.macosx-10.9-x86_64-3.6/DIVA_EphGen_f77module.c" adding '/var/folders/65/7cxpqns144q6wx5lyb7zcyh80000gq/T/tmpkv4x9yg7/src.macosx-10.9-x86_64-3.6/fortranobject.c' to sources. adding '/var/folders/65/7cxpqns144q6wx5lyb7zcyh80000gq/T/tmpkv4x9yg7/src.macosx-10.9-x86_64-3.6' to include_dirs. copying /Users/user/anaconda3/lib/python3.6/site-packages/numpy/f2py/src/fortranobject.c -> /var/folders/65/7cxpqns144q6wx5lyb7zcyh80000gq/T/tmpkv4x9yg7/src.macosx-10.9-x86_64-3.6 copying /Users/user/anaconda3/lib/python3.6/site-packages/numpy/f2py/src/fortranobject.h -> /var/folders/65/7cxpqns144q6wx5lyb7zcyh80000gq/T/tmpkv4x9yg7/src.macosx-10.9-x86_64-3.6 build_src: building npy-pkg config files running build_ext new_compiler returns <class 'distutils.unixccompiler.UnixCCompiler'> customize UnixCCompiler customize UnixCCompiler using build_ext ******************************************************************************** <class 'distutils.unixccompiler.UnixCCompiler'> preprocessor = ['x86_64-apple-darwin13.4.0-clang', '-E', '-D_FORTIFY_SOURCE=2', '-mmacosx-version-min=10.9'] compiler = ['x86_64-apple-darwin13.4.0-clang', '-DNDEBUG', '-fwrapv', '-O2', '-Wall', '-Wstrict-prototypes', '-march=core2', '-mtune=haswell', '-mssse3', '-ftree-vectorize', '-fPIC', '-fPIE', '-fstack-protector-strong', '-O2', '-pipe', '-D_FORTIFY_SOURCE=2', '-mmacosx-version-min=10.9'] compiler_so = ['x86_64-apple-darwin13.4.0-clang', '-DNDEBUG', '-fwrapv', '-O2', '-Wall', '-Wstrict-prototypes', '-march=core2', '-mtune=haswell', '-mssse3', '-ftree-vectorize', '-fPIC', '-fPIE', '-fstack-protector-strong', '-O2', '-pipe', '-D_FORTIFY_SOURCE=2', '-mmacosx-version-min=10.9'] compiler_cxx = ['x86_64-apple-darwin13.4.0-clang++'] linker_so = ['x86_64-apple-darwin13.4.0-clang', '-bundle', '-undefined', 'dynamic_lookup', '-Wl,-pie', '-Wl,-headerpad_max_install_names', '-Wl,-rpath,/Users/user/anaconda3/lib', '-L/Users/user/anaconda3/lib', '-Wl,-pie', '-Wl,-headerpad_max_install_names', '-Wl,-rpath,/Users/user/anaconda3/lib', '-L/Users/user/anaconda3/lib', '-Wl,-export_dynamic', '-Wl,-pie', '-Wl,-headerpad_max_install_names', '-Wl,-dead_strip_dylibs', '-march=core2', '-mtune=haswell', '-mssse3', '-ftree-vectorize', '-fPIC', '-fPIE', '-fstack-protector-strong', '-O2', '-pipe', '-D_FORTIFY_SOURCE=2', '-mmacosx-version-min=10.9'] linker_exe = ['x86_64-apple-darwin13.4.0-clang'] archiver = ['/Users/user/anaconda3/bin/x86_64-apple-darwin13.4.0-ar', 'rc'] ranlib = ['ranlib'] libraries = [] library_dirs = [] include_dirs = ['/Users/user/anaconda3/include/python3.6m'] ******************************************************************************** customize AbsoftFCompiler find_executable('/Users/user/anaconda3/bin/x86_64-apple-darwin13.4.0-gfortran') Found executable /Users/user/anaconda3/bin/x86_64-apple-darwin13.4.0-gfortran find_executable('/Applications/Absoft19.0/bin/af77') Found executable /Applications/Absoft19.0/bin/af77 find_executable('/Users/user/anaconda3/bin/x86_64-apple-darwin13.4.0-ar') Found executable /Users/user/anaconda3/bin/x86_64-apple-darwin13.4.0-ar find_executable('/Users/user/anaconda3/bin/x86_64-apple-darwin13.4.0-ranlib') Found executable /Users/user/anaconda3/bin/x86_64-apple-darwin13.4.0-ranlib x86_64-apple-darwin13.4.0-gfortran: error: unrecognized command line option ‘-V’ Traceback (most recent call last): File "/Users/user/anaconda3/bin/f2py", line 11, in <module> load_entry_point('numpy==1.16.3', 'console_scripts', 'f2py')() File "/Users/user/anaconda3/lib/python3.6/site-packages/numpy/f2py/f2py2e.py", line 683, in main run_compile() File "/Users/user/anaconda3/lib/python3.6/site-packages/numpy/f2py/f2py2e.py", line 650, in run_compile setup(ext_modules=[ext]) File "/Users/user/anaconda3/lib/python3.6/site-packages/numpy/distutils/core.py", line 171, in setup return old_setup(**new_attr) File "/Users/user/anaconda3/lib/python3.6/distutils/core.py", line 148, in setup dist.run_commands() File "/Users/user/anaconda3/lib/python3.6/distutils/dist.py", line 955, in run_commands self.run_command(cmd) File "/Users/user/anaconda3/lib/python3.6/distutils/dist.py", line 974, in run_command cmd_obj.run() File "/Users/user/anaconda3/lib/python3.6/site-packages/numpy/distutils/command/build.py", line 47, in run old_build.run(self) File "/Users/user/anaconda3/lib/python3.6/distutils/command/build.py", line 135, in run self.run_command(cmd_name) File "/Users/user/anaconda3/lib/python3.6/distutils/cmd.py", line 313, in run_command self.distribution.run_command(command) File "/Users/user/anaconda3/lib/python3.6/distutils/dist.py", line 974, in run_command cmd_obj.run() File "/Users/user/anaconda3/lib/python3.6/site-packages/numpy/distutils/command/build_ext.py", line 226, in run fcompiler.customize(self.distribution) File "/Users/user/anaconda3/lib/python3.6/site-packages/numpy/distutils/fcompiler/__init__.py", line 479, in customize f77flags = self.flag_vars.f77 File "/Users/user/anaconda3/lib/python3.6/site-packages/numpy/distutils/fcompiler/environment.py", line 40, in __getattr__ return self._get_var(name, conf_desc) File "/Users/user/anaconda3/lib/python3.6/site-packages/numpy/distutils/fcompiler/environment.py", line 56, in _get_var var = self._hook_handler(name, hook) File "/Users/user/anaconda3/lib/python3.6/site-packages/numpy/distutils/fcompiler/__init__.py", line 709, in _environment_hook return hook() File "/Users/user/anaconda3/lib/python3.6/site-packages/numpy/distutils/fcompiler/absoft.py", line 119, in get_flags_f77 v = self.get_version() File "/Users/user/anaconda3/lib/python3.6/site-packages/numpy/distutils/fcompiler/__init__.py", line 432, in get_version raise CompilerNotFound() numpy.distutils.fcompiler.CompilerNotFound (base) Samuels-Mac-Pro:2nd_ODE_auto_deriv_cleanup_Rev-07 user$ A copy of the Fortran code and its corresponding .pyf file have been attached to this note. Any suggestions as to how to fix this problem. Sam Dupree.

2 years, 7 months

Sebastian Berg at BIDS

by Stefan van der Walt

Hi everyone, We are happy to announce that Sebastian Berg has joined the team at BIDS, and will be working full-time on NumPy for the next two years. Sebastian has been a core contributor to the project for seven years already, and we are delighted that he will now be able to spend even more of his effort here. Sebastian will be working on the dtype refactoring and, as the rest of the team, be supporting the NumPy community in whichever ways we can. Please join the community call this Wednesday to welcome Sebastian on board. As always, your feedback on our work is much appreciated! Best regards, Stéfan

2 years, 7 months

2019 John Hunter Excellence in Plotting Contest Reminder

by Madicken Munk

Hi everybody, I'd like to remind you all about the 2019 John Hunter Excellence in Plotting Contest. My apologies to those of you getting this on multiple lists. In memory of John Hunter, we are pleased to be reviving the SciPy John Hunter Excellence in Plotting Competition for 2019. This open competition aims to highlight the importance of data visualization to scientific progress and showcase the capabilities of open source software. Participants are invited to submit scientific plots to be judged by a panel. The winning entries will be announced and displayed at the conference. John Hunter’s family and NumFocus are graciously sponsoring cash prizes for the winners in the following amounts: - 1st prize: $1000 - 2nd prize: $750 - 3rd prize: $500 - Entries must be submitted by June, 8th to the form at https://goo.gl/forms/cFTB3FUBrMPfQ7Vz1 - Winners will be announced at Scipy 2019 in Austin, TX. - Participants do not need to attend the Scipy conference. - Entries may take the definition of “visualization” rather broadly. Entries may be, for example, a traditional printed plot, an interactive visualization for the web, or an animation. - Source code for the plot must be provided, in the form of Python code and/or a Jupyter notebook, along with a rendering of the plot in a widely used format. This may be, for example, PDF for print, standalone HTML and Javascript for an interactive plot, or MPEG-4 for a video. If the original data can not be shared for reasons of size or licensing, "fake" data may be substituted, along with an image of the plot using real data. - Each entry must include a 300-500 word abstract describing the plot and its importance for a general scientific audience. - Entries will be judged on their clarity, innovation and aesthetics, but most importantly for their effectiveness in communicating a real-world problem. Entrants are encouraged to submit plots that were used during the course of research or work, rather than merely being hypothetical. - SciPy reserves the right to display any and all entries, whether prize-winning or not, at the conference, use in any materials or on its website, with attribution to the original author(s). SciPy John Hunter Excellence in Plotting Competition Co-Chairs Hannah Aizenman Thomas Caswell Madicken Munk Nelle Varoquaux

2 years, 7 months

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

NumPy-Discussion April 2019