Mailman 3 September 2017 - NumPy-Discussion

mixed mode arithmetic
by Neal Becker July 11, 2023

July 11, 2023

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?

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Invalid value encoutered : how to prevent numpy.where to do this?
by Eric Emsellem Feb. 18, 2023

Feb. 18, 2023

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 … [View More]

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Re: [Numpy-discussion] example reading binary Fortran file
by Neil Martinsen-Burrell July 22, 2021

July 22, 2021

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 … [View More]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 [View Less]

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NumPy 1.13.3 released.
by Charles R Harris Sept. 29, 2017

Sept. 29, 2017

HI All, On behalf of the NumPy team, I am pleased to annouce the release of Numpy 1.13.3. This is a re-release of 1.13.2, which suffered from compatibility problems, see issue 9786 <https://github.com/numpy/numpy/issues/9786>. It is a bugfix release for some problems found since 1.13.1. The most important fixes are for CVE-2017-12852 and the new temporary elision. Users of earlier versions of 1.13 should upgrade. The Python versions supported are 2.7 and 3.4 - 3.6. The Python 3.6 … [View More]wheels available from PIP are built with Python 3.6.2 and should be compatible with all previous versions of Python 3.6. It was cythonized with Cython 0.26.1, which should be free of the bugs found in 0.27 while also being compatible with Python 3.7-dev. The Windows wheels were built with OpenBlas instead ATLAS, which should improve the performance of the linear algebra functions. Wheels and zip archives are available from PyPI <https://pypi.python.org/pypi?:action=files&name=numpy&version=1.13.3>, both zip and tar archives are available from Github <https://github.com/numpy/numpy/releases/tag/v1.13.3>. Contributors ============ A total of 12 people contributed to this release. People with a "+" by their names contributed a patch for the first time. * Allan Haldane * Brandon Carter * Charles Harris * Eric Wieser * Iryna Shcherbina + * James Bourbeau + * Jonathan Helmus * Julian Taylor * Matti Picus * Michael Lamparski + * Michael Seifert * Ralf Gommers Pull requests merged ==================== A total of 20 pull requests were merged for this release. * #9390 BUG: Return the poly1d coefficients array directly * #9555 BUG: Fix regression in 1.13.x in distutils.mingw32ccompiler. * #9556 BUG: Fix true_divide when dtype=np.float64 specified. * #9557 DOC: Fix some rst markup in numpy/doc/basics.py. * #9558 BLD: Remove -xhost flag from IntelFCompiler. * #9559 DOC: Removes broken docstring example (source code, png, pdf)... * #9580 BUG: Add hypot and cabs functions to WIN32 blacklist. * #9732 BUG: Make scalar function elision check if temp is writeable. * #9736 BUG: Various fixes to np.gradient * #9742 BUG: Fix np.pad for CVE-2017-12852 * #9744 BUG: Check for exception in sort functions, add tests * #9745 DOC: Add whitespace after "versionadded::" directive so it actually... * #9746 BUG: Memory leak in np.dot of size 0 * #9747 BUG: Adjust gfortran version search regex * #9757 BUG: Cython 0.27 breaks NumPy on Python 3. * #9764 BUG: Ensure `_npy_scaled_cexp{,f,l}` is defined when needed. * #9765 BUG: PyArray_CountNonzero does not check for exceptions * #9766 BUG: Fixes histogram monotonicity check for unsigned bin values * #9767 BUG: Ensure consistent result dtype of count_nonzero * #9771 BUG, MAINT: Fix mtrand for Cython 0.27. Enjoy Chuck [View Less]

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Numpy wheels, openBLAS and threading
by Jean-Christophe Houde Sept. 28, 2017

Sept. 28, 2017

Hi all, not sure if this is the best place to ask for this. If not, please advise on the correct place. Since the numpy wheels internally use openBLAS, operations can be implicitly multithreaded directly by openBLAS. This, of course, can clash with multithreading or parallel processing. The recommended practice in this case is to set export OPENBLAS_NUM_THREADS=1 in the environment. However, I would like to be able to adjust this directly in my python code. Is there a way to control this … [View More]

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NumPy 1.13.2 released.
by Charles R Harris Sept. 27, 2017

Sept. 27, 2017

HI All, On behalf of the NumPy team, I am pleased to annouce the release of Numpy 1.13.2. This is a bugfix release for some problems found since 1.13.1. The most important fixes are for CVE-2017-12852 and temporary elision. Users of earlier versions of 1.13 should upgrade. The Python versions supported are 2.7 and 3.4 - 3.6. The Python 3.6 wheels available from PIP are built with Python 3.6.2 and should be compatible with all previous versions of Python 3.6. The Windows wheels are now built … [View More]

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ANN: first SciPy 1.0.0 release candidate
by Ralf Gommers Sept. 27, 2017

Sept. 27, 2017

Hi all, I'm excited to be able to announce the availability of the first release candidate of Scipy 1.0. This is a big release, and a version number that has been 16 years in the making. It contains a few more deprecations and backwards incompatible changes than an average release. Therefore please do test it on your own code, and report any issues on the Github issue tracker or on the scipy-dev mailing list. Sources and binary wheels can be found at https://pypi.python.org/pypi/scipy and … [View More]https://github.com/scipy/scipy/releases/tag/v1.0.0rc1. To install with pip: pip install --pre --upgrade scipy Thanks to everyone who contributed to this release! Ralf Pull requests merged after v1.0.0b1: - `#7876 <https://github.com/scipy/scipy/pull/7876>`__: GEN: Add comments to the tests for clarification - `#7891 <https://github.com/scipy/scipy/pull/7891>`__: ENH: backport #7879 to 1.0.x - `#7902 <https://github.com/scipy/scipy/pull/7902>`__: MAINT: signal: Make freqz handling of multidim. arrays match... - `#7905 <https://github.com/scipy/scipy/pull/7905>`__: REV: restore wminkowski - `#7908 <https://github.com/scipy/scipy/pull/7908>`__: FIX: Avoid bad ``__del__`` (close) behavior - `#7918 <https://github.com/scipy/scipy/pull/7918>`__: TST: mark two optimize.linprog tests as xfail. See gh-7877. - `#7929 <https://github.com/scipy/scipy/pull/7929>`__: MAINT: changed defaults to lower in sytf2, sytrf and hetrf - `#7938 <https://github.com/scipy/scipy/pull/7938>`__: MAINT: backports from 1.0.x - `#7939 <https://github.com/scipy/scipy/pull/7939>`__: Fix umfpack solver construction for win-amd64 ========================== SciPy 1.0.0 Release Notes ========================== .. note:: Scipy 1.0.0 is not released yet! .. contents:: SciPy 1.0.0 is the culmination of 8 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Moreover, our development attention will now shift to bug-fix releases on the 1.0.x branch, and on adding new features on the master branch. Some of the highlights of this release are: - Major build improvements. Windows wheels are available on PyPI for the first time, and continuous integration has been set up on Windows and OS X in addition to Linux. - A set of new ODE solvers and a unified interface to them (`scipy.integrate.solve_ivp`). - Two new trust region optimizers and a new linear programming method, with improved performance compared to what `scipy.optimize` offered previously. - Many new BLAS and LAPACK functions were wrapped. The BLAS wrappers are now complete. This release requires Python 2.7 or 3.4+ and NumPy 1.8.2 or greater. This is also the last release to support LAPACK 3.1.x - 3.3.x. Moving the lowest supported LAPACK version to >3.2.x was long blocked by Apple Accelerate providing the LAPACK 3.2.1 API. We have decided that it's time to either drop Accelerate or, if there is enough interest, provide shims for functions added in more recent LAPACK versions so it can still be used. New features ============ `scipy.cluster` improvements ---------------------------- `scipy.cluster.hierarchy.optimal_leaf_ordering`, a function to reorder a linkage matrix to minimize distances between adjacent leaves, was added. `scipy.fftpack` improvements ---------------------------- N-dimensional versions of the discrete sine and cosine transforms and their inverses were added as ``dctn``, ``idctn``, ``dstn`` and ``idstn``. `scipy.integrate` improvements ------------------------------ A set of new ODE solvers have been added to `scipy.integrate`. The convenience function `scipy.integrate.solve_ivp` allows uniform access to all solvers. The individual solvers (``RK23``, ``RK45``, ``Radau``, ``BDF`` and ``LSODA``) can also be used directly. `scipy.linalg` improvements ---------------------------- The BLAS wrappers in `scipy.linalg.blas` have been completed. Added functions are ``*gbmv``, ``*hbmv``, ``*hpmv``, ``*hpr``, ``*hpr2``, ``*spmv``, ``*spr``, ``*tbmv``, ``*tbsv``, ``*tpmv``, ``*tpsv``, ``*trsm``, ``*trsv``, ``*sbmv``, ``*spr2``, Wrappers for the LAPACK functions ``*gels``, ``*stev``, ``*sytrd``, ``*hetrd``, ``*sytf2``, ``*hetrf``, ``*sytrf``, ``*sycon``, ``*hecon``, ``*gglse``, ``*stebz``, ``*stemr``, ``*sterf``, and ``*stein`` have been added. The function `scipy.linalg.subspace_angles` has been added to compute the subspace angles between two matrices. The function `scipy.linalg.clarkson_woodruff_transform` has been added. It finds low-rank matrix approximation via the Clarkson-Woodruff Transform. The functions `scipy.linalg.eigh_tridiagonal` and `scipy.linalg.eigvalsh_tridiagonal`, which find the eigenvalues and eigenvectors of tridiagonal hermitian/symmetric matrices, were added. `scipy.ndimage` improvements ---------------------------- Support for homogeneous coordinate transforms has been added to `scipy.ndimage.affine_transform`. The ``ndimage`` C code underwent a significant refactoring, and is now a lot easier to understand and maintain. `scipy.optimize` improvements ----------------------------- The methods ``trust-region-exact`` and ``trust-krylov`` have been added to the function `scipy.optimize.minimize`. These new trust-region methods solve the subproblem with higher accuracy at the cost of more Hessian factorizations (compared to dogleg) or more matrix vector products (compared to ncg) but usually require less nonlinear iterations and are able to deal with indefinite Hessians. They seem very competitive against the other Newton methods implemented in scipy. `scipy.optimize.linprog` gained an interior point method. Its performance is superior (both in accuracy and speed) to the older simplex method. `scipy.signal` improvements --------------------------- An argument ``fs`` (sampling frequency) was added to the following functions: ``firwin``, ``firwin2``, ``firls``, and ``remez``. This makes these functions consistent with many other functions in `scipy.signal` in which the sampling frequency can be specified. `scipy.signal.freqz` has been sped up significantly for FIR filters. `scipy.sparse` improvements --------------------------- Iterating over and slicing of CSC and CSR matrices is now faster by up to ~35%. The ``tocsr`` method of COO matrices is now several times faster. The ``diagonal`` method of sparse matrices now takes a parameter, indicating which diagonal to return. `scipy.sparse.linalg` improvements ---------------------------------- A new iterative solver for large-scale nonsymmetric sparse linear systems, `scipy.sparse.linalg.gcrotmk`, was added. It implements ``GCROT(m,k)``, a flexible variant of ``GCROT``. `scipy.sparse.linalg.lsmr` now accepts an initial guess, yielding potentially faster convergence. SuperLU was updated to version 5.2.1. `scipy.spatial` improvements ---------------------------- Many distance metrics in `scipy.spatial.distance` gained support for weights. The signatures of `scipy.spatial.distance.pdist` and `scipy.spatial.distance.cdist` were changed to ``*args, **kwargs`` in order to support a wider range of metrics (e.g. string-based metrics that need extra keywords). Also, an optional ``out`` parameter was added to ``pdist`` and ``cdist`` allowing the user to specify where the resulting distance matrix is to be stored `scipy.stats` improvements -------------------------- The methods ``cdf`` and ``logcdf`` were added to `scipy.stats.multivariate_normal`, providing the cumulative distribution function of the multivariate normal distribution. New statistical distance functions were added, namely `scipy.stats.wasserstein_distance` for the first Wasserstein distance and `scipy.stats.energy_distance` for the energy distance. Deprecated features =================== The following functions in `scipy.misc` are deprecated: ``bytescale``, ``fromimage``, ``imfilter``, ``imread``, ``imresize``, ``imrotate``, ``imsave``, ``imshow`` and ``toimage``. Most of those functions have unexpected behavior (like rescaling and type casting image data without the user asking for that). Other functions simply have better alternatives. ``scipy.interpolate.interpolate_wrapper`` and all functions in that submodule are deprecated. This was a never finished set of wrapper functions which is not relevant anymore. The ``fillvalue`` of `scipy.signal.convolve2d` will be cast directly to the dtypes of the input arrays in the future and checked that it is a scalar or an array with a single element. ``scipy.spatial.distance.matching`` is deprecated. It is an alias of `scipy.spatial.distance.hamming`, which should be used instead. Implementation of `scipy.spatial.distance.wminkowski` was based on a wrong interpretation of the metric definition. In scipy 1.0 it has been just deprecated in the documentation to keep retro-compatibility but is recommended to use the new version of `scipy.spatial.distance.minkowski` that implements the correct behaviour. Positional arguments of `scipy.spatial.distance.pdist` and `scipy.spatial.distance.cdist` should be replaced with their keyword version. Backwards incompatible changes ============================== The following deprecated functions have been removed from `scipy.stats`: ``betai``, ``chisqprob``, ``f_value``, ``histogram``, ``histogram2``, ``pdf_fromgamma``, ``signaltonoise``, ``square_of_sums``, ``ss`` and ``threshold``. The following deprecated functions have been removed from `scipy.stats.mstats`: ``betai``, ``f_value_wilks_lambda``, ``signaltonoise`` and ``threshold``. The deprecated ``a`` and ``reta`` keywords have been removed from `scipy.stats.shapiro`. The deprecated functions ``sparse.csgraph.cs_graph_components`` and ``sparse.linalg.symeig`` have been removed from `scipy.sparse`. The following deprecated keywords have been removed in `scipy.sparse.linalg`: ``drop_tol`` from ``splu``, and ``xtype`` from ``bicg``, ``bicgstab``, ``cg``, ``cgs``, ``gmres``, ``qmr`` and ``minres``. The deprecated functions ``expm2`` and ``expm3`` have been removed from `scipy.linalg`. The deprecated keyword ``q`` was removed from `scipy.linalg.expm`. And the deprecated submodule ``linalg.calc_lwork`` was removed. The deprecated functions ``C2K``, ``K2C``, ``F2C``, ``C2F``, ``F2K`` and ``K2F`` have been removed from `scipy.constants`. The deprecated ``ppform`` class was removed from `scipy.interpolate`. The deprecated keyword ``iprint`` was removed from `scipy.optimize.fmin_cobyla`. The default value for the ``zero_phase`` keyword of `scipy.signal.decimate` has been changed to True. The ``kmeans`` and ``kmeans2`` functions in `scipy.cluster.vq` changed the method used for random initialization, so using a fixed random seed will not necessarily produce the same results as in previous versions. `scipy.special.gammaln` does not accept complex arguments anymore. The deprecated functions ``sph_jn``, ``sph_yn``, ``sph_jnyn``, ``sph_in``, ``sph_kn``, and ``sph_inkn`` have been removed. Users should instead use the functions ``spherical_jn``, ``spherical_yn``, ``spherical_in``, and ``spherical_kn``. Be aware that the new functions have different signatures. The cross-class properties of `scipy.signal.lti` systems have been removed. The following properties/setters have been removed: Name - (accessing/setting has been removed) - (setting has been removed) * StateSpace - (``num``, ``den``, ``gain``) - (``zeros``, ``poles``) * TransferFunction (``A``, ``B``, ``C``, ``D``, ``gain``) - (``zeros``, ``poles``) * ZerosPolesGain (``A``, ``B``, ``C``, ``D``, ``num``, ``den``) - () ``signal.freqz(b, a)`` with ``b`` or ``a`` >1-D raises a ``ValueError``. This was a corner case for which it was unclear that the behavior was well-defined. The method ``var`` of `scipy.stats.dirichlet` now returns a scalar rather than an ndarray when the length of alpha is 1. Other changes ============= SciPy now has a formal governance structure. It consists of a BDFL (Pauli Virtanen) and a Steering Committee. See `the governance document < https://github.com/scipy/scipy/blob/master/doc/source/dev/governance/govern… >`_ for details. It is now possible to build SciPy on Windows with MSVC + gfortran! Continuous integration has been set up for this build configuration on Appveyor, building against OpenBLAS. Continuous integration for OS X has been set up on TravisCI. The SciPy test suite has been migrated from ``nose`` to ``pytest``. ``scipy/_distributor_init.py`` was added to allow redistributors of SciPy to add custom code that needs to run when importing SciPy (e.g. checks for hardware, DLL search paths, etc.). Support for PEP 518 (specifying build system requirements) was added - see ``pyproject.toml`` in the root of the SciPy repository. In order to have consistent function names, the function ``scipy.linalg.solve_lyapunov`` is renamed to `scipy.linalg.solve_continuous_lyapunov`. The old name is kept for backwards-compatibility. [View Less]

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Re: [Numpy-discussion] floor with dtype
by Mark Bakker Sept. 27, 2017

Sept. 27, 2017

> On 2017-09-25 10:59, Renato Fabbri wrote: > > > """ > > > In [3]: n.floor(n.linspace(0,5,7), dtype=n.int <http://n.int>) > > > ------------------------------------------------------------ > > --------------- > > > TypeError Traceback (most recent call > > last) > > > <ipython-input-3-1471f5d738e8> in <module>() > > > ----> 1 n.floor(n.linspace(0,5,7), dtype=n.int <http:… [View More]

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Proposal - change to OpenBLAS for Windows wheels
by Matthew Brett Sept. 25, 2017

Sept. 25, 2017

Hi, I suggest we switch from ATLAS to OpenBLAS for our Windows wheels: * OpenBLAS is much faster, at least when Tony Kelman tested it last year [1]; * We now have an automated Appveyor build for OpenBLAS [2, 3]; * Tests are passing with 32-bit and 64-bit wheels [4]; * The next Scipy release will have OpenBLAS wheels; Any objections / questions / alternatives? Cheers, Matthew [1] https://github.com/numpy/numpy/issues/5479#issuecomment-185033668 [2] https://github.com/matthew-brett/build-… [View More]

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nditer and updateifcopy semantics - advice needed
by Matti Picus Sept. 25, 2017

Sept. 25, 2017

I filed issue 9714 trying to get some feedback on what to do with updateifcopy semantics and user-exposed nditer. For those who are unfamiliar with the issue see below for a short summary, issue 7054 for a lengthy discussion, or pull request 9639 (which is still not merged). As I mention in the issue, I am willing to put in the work to make the magical update done in the last line of this snippet more explicit: a = arange(24, dtype='<i4').reshape(2, 3, 4) i = nditer(a, ['buffered'], … [View More]

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