Mailman 3 February 2017 - NumPy-Discussion

ANN: scipy 0.19.0 release candidate 1
by Evgeni Burovski Feb. 15, 2017

Feb. 15, 2017

Hi, I'm pleased to announce the availability of the first release candidate for scipy 0.19.0. It contains contributions from 120 people over the course of seven months. Please try this release and report any issues on Github tracker, https://github.com/scipy/scipy, or scipy-dev mailing list. Source tarballs and release notes are available from Github releases, https://github.com/scipy/scipy/releases/tag/v0.19.0rc1 Please note that this is a source-only release. We do not provide Windows binaries for this release. OS X and Linux wheels will be provided for the final release. The current release schedule is 22 Feb : 0.19.0rc2, if needed 09 Mar : 0.19.0 final Thanks to everyone who contributed to this release! Cheers, Evgeni A part of the release notes follows below: =================================================== ========================== SciPy 0.19.0 Release Notes ========================== .. note:: Scipy 0.19.0 is not released yet! .. contents:: SciPy 0.19.0 is the culmination of X months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Moreover, our development attention will now shift to bug-fix releases on the 0.19.x branch, and on adding new features on the master branch. This release requires Python 2.7 or 3.4-3.6 and NumPy 1.8.2 or greater. Highlights of this release include: - A unified foreign function interface layer, `scipy.LowLevelCallable`. - Cython API for scalar, typed versions of the universal functions from the `scipy.special` module, via `cimport scipy.special.cython_special`. New features ============ Foreign function interface improvements --------------------------------------- `scipy.LowLevelCallable` provides a new unified interface for wrapping low-level compiled callback functions in the Python space. It supports Cython imported "api" functions, ctypes function pointers, CFFI function pointers, ``PyCapsules``, Numba jitted functions and more. See `gh-6509 <https://github.com/scipy/scipy/pull/6509>`_ for details. `scipy.linalg` improvements --------------------------- The function `scipy.linalg.solve` obtained two more keywords ``assume_a`` and ``transposed``. The underlying LAPACK routines are replaced with "expert" versions and now can also be used to solve symmetric, hermitian and positive definite coefficient matrices. Moreover, ill-conditioned matrices now cause a warning to be emitted with the estimated condition number information. Old ``sym_pos`` keyword is kept for backwards compatibility reasons however it is identical to using ``assume_a='pos'``. Moreover, the ``debug`` keyword, which had no function but only printing the ``overwrite_<a, b>`` values, is deprecated. The function `scipy.linalg.matrix_balance` was added to perform the so-called matrix balancing using the LAPACK xGEBAL routine family. This can be used to approximately equate the row and column norms through diagonal similarity transformations. The functions `scipy.linalg.solve_continuous_are` and `scipy.linalg.solve_discrete_are` have numerically more stable algorithms. These functions can also solve generalized algebraic matrix Riccati equations. Moreover, both gained a ``balanced`` keyword to turn balancing on and off. `scipy.spatial` improvements ---------------------------- `scipy.spatial.SphericalVoronoi.sort_vertices_of_regions` has been re-written in Cython to improve performance. `scipy.spatial.SphericalVoronoi` can handle > 200 k points (at least 10 million) and has improved performance. The function `scipy.spatial.distance.directed_hausdorff` was added to calculate the directed Hausdorff distance. ``count_neighbors`` method of `scipy.spatial.cKDTree` gained an ability to perform weighted pair counting via the new keywords ``weights`` and ``cumulative``. See `gh-5647 <https://github.com/scipy/scipy/pull/5647>`_ for details. `scipy.ndimage` improvements ---------------------------- The callback function C API supports PyCapsules in Python 2.7 Multidimensional filters now allow having different extrapolation modes for different axes. `scipy.optimize` improvements ----------------------------- The `scipy.optimize.basinhopping` global minimizer obtained a new keyword, `seed`, which can be used to seed the random number generator and obtain repeatable minimizations. The keyword `sigma` in `scipy.optimize.curve_fit` was overloaded to also accept the covariance matrix of errors in the data. `scipy.signal` improvements --------------------------- The function `scipy.signal.correlate` and `scipy.signal.convolve` have a new optional parameter `method`. The default value of `auto` estimates the fastest of two computation methods, the direct approach and the Fourier transform approach. A new function has been added to choose the convolution/correlation method, `scipy.signal.choose_conv_method` which may be appropriate if convolutions or correlations are performed on many arrays of the same size. New functions have been added to calculate complex short time fourier transforms of an input signal, and to invert the transform to recover the original signal: `scipy.signal.stft` and `scipy.signal.istft`. This implementation also fixes the previously incorrect ouput of `scipy.signal.spectrogram` when complex output data were requested. The function `scipy.signal.sosfreqz` was added to compute the frequency response from second-order sections. The function `scipy.signal.unit_impulse` was added to conveniently generate an impulse function. The function `scipy.signal.iirnotch` was added to design second-order IIR notch filters that can be used to remove a frequency component from a signal. The dual function `scipy.signal.iirpeak` was added to compute the coefficients of a second-order IIR peak (resonant) filter. The function `scipy.signal.minimum_phase` was added to convert linear-phase FIR filters to minimum phase. The functions `scipy.signal.upfirdn` and `scipy.signal.resample_poly` are now substantially faster when operating on some n-dimensional arrays when n > 1. The largest reduction in computation time is realized in cases where the size of the array is small (<1k samples or so) along the axis to be filtered. `scipy.fftpack` improvements ---------------------------- Fast Fourier transform routines now accept `np.float16` inputs and upcast them to `np.float32`. Previously, they would raise an error. `scipy.cluster` improvements ---------------------------- Methods ``"centroid"`` and ``"median"`` of `scipy.cluster.hierarchy.linkage` have been significantly sped up. Long-standing issues with using ``linkage`` on large input data (over 16 GB) have been resolved. `scipy.sparse` improvements --------------------------- The functions `scipy.sparse.save_npz` and `scipy.sparse.load_npz` were added, providing simple serialization for some sparse formats. The `prune` method of classes `bsr_matrix`, `csc_matrix`, and `csr_matrix` was updated to reallocate backing arrays under certain conditions, reducing memory usage. The methods `argmin` and `argmax` were added to classes `coo_matrix`, `csc_matrix`, `csr_matrix`, and `bsr_matrix`. New function `scipy.sparse.csgraph.structural_rank` computes the structural rank of a graph with a given sparsity pattern. New function `scipy.sparse.linalg.spsolve_triangular` solves a sparse linear system with a triangular left hand side matrix. `scipy.special` improvements ---------------------------- Scalar, typed versions of universal functions from `scipy.special` are available in the Cython space via ``cimport`` from the new module `scipy.special.cython_special`. These scalar functions can be expected to be significantly faster then the universal functions for scalar arguments. See the `scipy.special` tutorial for details. Better control over special-function errors is offered by the functions `scipy.special.geterr` and `scipy.special.seterr` and the context manager `scipy.special.errstate`. The names of orthogonal polynomial root functions have been changed to be consistent with other functions relating to orthogonal polynomials. For example, `scipy.special.j_roots` has been renamed `scipy.special.roots_jacobi` for consistency with the related functions `scipy.special.jacobi` and `scipy.special.eval_jacobi`. To preserve back-compatibility the old names have been left as aliases. Wright Omega function is implemented as `scipy.special.wrightomega`. `scipy.stats` improvements -------------------------- The function `scipy.stats.weightedtau` was added. It provides a weighted version of Kendall's tau. New class `scipy.stats.multinomial` implements the multinomial distribution. New class `scipy.stats.rv_histogram` constructs a continuous univariate distribution with a piecewise linear CDF from a binned data sample. New class `scipy.stats.argus` implements the Argus distribution. `scipy.interpolate` improvements -------------------------------- New class `scipy.interpolate.BSpline` represents splines. ``BSpline`` objects contain knots and coefficients and can evaluate the spline. The format is consistent with FITPACK, so that one can do, for example:: >>> t, c, k = splrep(x, y, s=0) >>> spl = BSpline(t, c, k) >>> np.allclose(spl(x), y) ``spl*`` functions, `scipy.interpolate.splev`, `scipy.interpolate.splint`, `scipy.interpolate.splder` and `scipy.interpolate.splantider`, accept both ``BSpline`` objects and ``(t, c, k)`` tuples for backwards compatibility. For multidimensional splines, ``c.ndim > 1``, ``BSpline`` objects are consistent with piecewise polynomials, `scipy.interpolate.PPoly`. This means that ``BSpline`` objects are not immediately consistent with `scipy.interpolate.splprep`, and one *cannot* do ``>>> BSpline(*splprep([x, y])[0])``. Consult the `scipy.interpolate` test suite for examples of the precise equivalence. In new code, prefer using ``scipy.interpolate.BSpline`` objects instead of manipulating ``(t, c, k)`` tuples directly. New function `scipy.interpolate.make_interp_spline` constructs an interpolating spline given data points and boundary conditions. New function `scipy.interpolate.make_lsq_spline` constructs a least-squares spline approximation given data points. `scipy.integrate` improvements ------------------------------ Now `scipy.integrate.fixed_quad` supports vector-valued functions. Deprecated features =================== `scipy.interpolate.splmake`, `scipy.interpolate.spleval` and `scipy.interpolate.spline` are deprecated. The format used by `splmake/spleval` was inconsistent with `splrep/splev` which was confusing to users. `scipy.special.errprint` is deprecated. Improved functionality is available in `scipy.special.seterr`. Backwards incompatible changes ============================== The deprecated ``scipy.weave`` submodule was removed. `scipy.spatial.distance.squareform` now returns arrays of the same dtype as the input, instead of always float64. `scipy.special.errprint` now returns a boolean. The function `scipy.signal.find_peaks_cwt` now returns an array instead of a list. `scipy.stats.kendalltau` now computes the correct p-value in case the input contains ties. The p-value is also identical to that computed by `scipy.stats.mstats.kendalltau` and by R. If the input does not contain ties there is no change w.r.t. the previous implementation. The function `scipy.linalg.block_diag` will not ignore zero-sized matrices anymore. Instead it will insert rows or columns of zeros of the appropriate size. See gh-4908 for more details. Other changes ============= SciPy wheels will now report their dependency on ``numpy`` on all platforms. This change was made because Numpy wheels are available, and because the pip upgrade behavior is finally changing for the better (use ``--upgrade-strategy=only-if-needed`` for ``pip >= 8.2``; that behavior will become the default in the next major version of ``pip``). Numerical values returned by `scipy.interpolate.interp1d` with ``kind="cubic"`` and ``"quadratic"`` may change relative to previous scipy versions. If your code depended on specific numeric values (i.e., on implementation details of the interpolators), you may want to double-check your results. Authors ======= * @endolith * Max Argus + * Hervé Audren * Alessandro Pietro Bardelli + * Michael Benfield + * Felix Berkenkamp * Matthew Brett * Per Brodtkorb * Evgeni Burovski * Pierre de Buyl * CJ Carey * Brandon Carter + * Tim Cera * Klesk Chonkin * Christian Häggström + * Luca Citi * Peadar Coyle + * Daniel da Silva + * Greg Dooper + * John Draper + * drlvk + * David Ellis + * Yu Feng * Baptiste Fontaine + * Jed Frey + * Siddhartha Gandhi + * GiggleLiu + * Wim Glenn + * Akash Goel + * Ralf Gommers * Alexander Goncearenco + * Richard Gowers + * Alex Griffing * Radoslaw Guzinski + * Charles Harris * Callum Jacob Hays + * Ian Henriksen * Randy Heydon + * Lindsey Hiltner + * Gerrit Holl + * Hiroki IKEDA + * jfinkels + * Mher Kazandjian + * Thomas Keck + * keuj6 + * Kornel Kielczewski + * Sergey B Kirpichev + * Vasily Kokorev + * Eric Larson * Denis Laxalde * Gregory R. Lee * Josh Lefler + * Julien Lhermitte + * Evan Limanto + * Nikolay Mayorov * Geordie McBain + * Josue Melka + * Matthieu Melot * michaelvmartin15 + * Surhud More + * Brett M. Morris + * Chris Mutel + * Paul Nation * Andrew Nelson * David Nicholson + * Aaron Nielsen + * Joel Nothman * nrnrk + * Juan Nunez-Iglesias * Mikhail Pak + * Gavin Parnaby + * Thomas Pingel + * Ilhan Polat + * Aman Pratik + * Sebastian Pucilowski * Ted Pudlik * puenka + * Eric Quintero * Tyler Reddy * Joscha Reimer * Antonio Horta Ribeiro + * Edward Richards + * Roman Ring + * Rafael Rossi + * Colm Ryan + * Sami Salonen + * Alvaro Sanchez-Gonzalez + * Johannes Schmitz * Kari Schoonbee * Yurii Shevchuk + * Jonathan Siebert + * Jonathan Tammo Siebert + * Scott Sievert + * Sourav Singh * Byron Smith + * Srikiran + * Samuel St-Jean + * Yoni Teitelbaum + * Bhavika Tekwani * Martin Thoma * timbalam + * Svend Vanderveken + * Sebastiano Vigna + * Aditya Vijaykumar + * Santi Villalba + * Ze Vinicius * Pauli Virtanen * Matteo Visconti * Yusuke Watanabe + * Warren Weckesser * Phillip Weinberg + * Nils Werner * Jakub Wilk * Josh Wilson * wirew0rm + * David Wolever + * Nathan Woods * ybeltukov + * G Young * Evgeny Zhurko + A total of 120 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.

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Marten van Kerkwijk added to numpy team.
by Charles R Harris Feb. 14, 2017

Feb. 14, 2017

Hi All, I'm pleased to welcome Marten to the numpy team. His reviews of PRs have been very useful in the past and I am happy that he has accepted our invitation to join the team. Cheers, Chuck

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zero values in the output of PyArray_AsCArray(
by Boukhdhir Amal Feb. 7, 2017

Feb. 7, 2017

Hi, I am trying to access an array as a C-Type using the function 'PyArray_AsCArray'The problem is that I am getting many 0 values in the resulting C- array. Some of the indexedvalues are correct. This is my code: static PyObject* cos_func_np(PyObject* self, PyObject* args){ PyObject *in_array_object; PyObject *out_array; int** segs_2d_array; /* Parse single numpy array argument*/ if (! PyArg_ParseTuple(args, "O", &in_array_object)) return NULL; int typenum = NPY_INT64; PyArray_Descr *descr; descr = PyArray_DescrFromType(typenum); npy_intp dims[2]; PyArray_AsCArray(&in_array_object, (void**) &segs_2d_array, dims, 2, descr); printf("\n-segs_2d_array: %d --\n", segs_2d_array[1][5]); //return Py_BuildValue("O", in_array_object); } For example:segs_2d_array[0][0] and segs_2d_array[1][2] outputs the correct values, however, segs_2d_array[1][3] and segs_2d_array[1][5] are equal to zero. What is wrong with this code please ?

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composing Euler rotation matrices
by Seb Feb. 1, 2017

Feb. 1, 2017

Hello, I'm trying to compose Euler rotation matrices shown in https://en.wikipedia.org/wiki/Euler_angles#Rotation_matrix. For example, The Z1Y2X3 Tait-Bryan rotation shown in the table can be represented in Numpy using the function: def z1y2x3(alpha, beta, gamma): """Rotation matrix given Euler angles""" return np.array([[np.cos(alpha) * np.cos(beta), np.cos(alpha) * np.sin(beta) * np.sin(gamma) - np.cos(gamma) * np.sin(alpha), np.sin(alpha) * np.sin(gamma) + np.cos(alpha) * np.cos(gamma) * np.sin(beta)], [np.cos(beta) * np.sin(alpha), np.cos(alpha) * np.cos(gamma) + np.sin(alpha) * np.sin(beta) * np.sin(gamma), np.cos(gamma) * np.sin(alpha) * np.sin(beta) - np.cos(alpha) * np.sin(gamma)], [-np.sin(beta), np.cos(beta) * np.sin(gamma), np.cos(beta) * np.cos(gamma)]]) which given alpha, beta, gamma as: angles = np.radians(np.array([30, 20, 10])) returns the following matrix: In [31]: z1y2x3(angles[0], angles[1], angles[2]) Out[31]: array([[ 0.81379768, -0.44096961, 0.37852231], [ 0.46984631, 0.88256412, 0.01802831], [-0.34202014, 0.16317591, 0.92541658]]) If I understand correctly, one should be able to compose this matrix by multiplying the rotation matrices that it is made of. However, I cannot reproduce this matrix via composition; i.e. by multiplying the underlying rotation matrices. Any tips would be appreciated. -- Seb

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ANN: xarray v0.9 released
by Stephan Hoyer Feb. 1, 2017

Feb. 1, 2017

I'm pleased to announce the release of the latest major version of xarray, v0.9. xarray is an open source project and Python package that provides a toolkit and data structures for N-dimensional labeled arrays. Its approach combines an API inspired by pandas with the Common Data Model for self-described scientific data. This release includes five months worth of enhancements and bug fixes from 24 contributors, including some significant enhancements to the data model that are not fully backwards compatible. Highlights include: - Coordinates are now optional in the xarray data model, even for dimensions. - Changes to caching, lazy loading and pickling to improve xarray’s experience for parallel computing. - Improvements for accessing and manipulating pandas.MultiIndex levels. - Many new methods and functions, including quantile(), cumsum(), cumprod(), combine_firstset_index(), reset_index(), reorder_levels(), full_like(), zeros_like(), ones_like(), open_dataarray(), compute(), Dataset.info(), testing.assert_equal(), testing.assert_identical(), and testing.assert_allclose(). For more details, read the full release notes: http://xarray.pydata.org/en/latest/whats-new.html You can install xarray with pip or conda: pip install xarray conda install -c conda-forge xarray Best, Stephan

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Fwd: Installation Problem
by Naresh P Feb. 1, 2017

Feb. 1, 2017

*Warm Regards,* *Naresh* *Developer * *Consumer Goods Myanmar Limited ( CGM )No.15, Junction Square Complex, 37-G, Pyay Road,**13041 Kamayut Township**, Yangon, Myanmar.* *Tel: +959979867914* http://www.cg-m.com ---------- Forwarded message ---------- From: <numpy-discussion-owner(a)scipy.org> Date: Wed, Feb 1, 2017 at 2:59 PM Subject: Fwd: Installation Problem To: naresh.p(a)okdollar.com This is a members-only list. Your message has been automatically rejected, since it came from a non-member's email address. Please make sure to use the email account that you used to join this list. ---------- Forwarded message ---------- From: Naresh P <naresh.p(a)okdollar.com> To: numpy-discussion(a)scipy.org Cc: Date: Wed, 1 Feb 2017 14:59:08 +0630 Subject: Fwd: Installation Problem *Warm Regards,* *Naresh* *Developer * *Consumer Goods Myanmar Limited ( CGM )No.15, Junction Square Complex, 37-G, Pyay Road,**13041 Kamayut Township**, Yangon, Myanmar.* *Tel: +959979867914* http://www.cg-m.com On Wed, Feb 1, 2017 at 2:54 PM, <numpy-discussion-owner(a)scipy.org> wrote: > This is a members-only list. Your message has been automatically > rejected, since it came from a non-member's email address. Please > make sure to use the email account that you used to join this list. > > > > ---------- Forwarded message ---------- > From: Naresh P <naresh.p(a)okdollar.com> > To: numpy-discussion(a)scipy.org > Cc: > Date: Wed, 1 Feb 2017 14:54:21 +0630 > Subject: Fwd: Installation Problem > Hi Team, > > i have still that problem , can please help me. > > let me know , i don't know how get ur membership , > > Thanks > > > > *Warm Regards,* > *Naresh* > *Developer * > *Consumer Goods Myanmar Limited ( CGM )No.15, Junction Square > Complex, 37-G, Pyay Road,**13041 Kamayut Township**, Yangon, Myanmar.* > *Tel: +959979867914* > http://www.cg-m.com > > ---------- Forwarded message ---------- > From: Naresh P <naresh.p(a)okdollar.com> > Date: Tue, Jan 31, 2017 at 3:20 PM > Subject: Installation Problem > To: numpy-discussion(a)scipy.org > > > > Hi , > i tried to somany times , but not install ,below msg are display. > help me > > > [image: Inline image 1] > > > *Warm Regards,* > *Naresh* > *Developer * > *Consumer Goods Myanmar Limited ( CGM )No.15, Junction Square > Complex, 37-G, Pyay Road,**13041 Kamayut Township**, Yangon, Myanmar.* > *Tel: +959979867914* > http://www.cg-m.com > > >

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