Mailman 3 May 2019 - SciPy-Dev

Backend support for fftpack - GSoC 2019
by Peter Bell June 10, 2019

June 10, 2019

Hello all, I'm happy to say my GSoC proposal was accepted, so I'll be working over the summer to add support for multiple fftpack backends. For those interested, I've extracted the main discussion from my proposal which you can read here: https://docs.google.com/document/d/1hGMc6ZrsmSi_LSixI5oFbo5cZkPUo92sfmnVthg… There are a several design decisions I raise in the proposal and any comments would be appreciated. Of particular note: * Should backends be allowed to add functionality or should they be completely interchangeable. E.g. unlike fftpack, FFTW has support for long doubles; should this be exposed to SciPy users? * Is there any interest in adding a SciPy config file? For just one option it's probably not worthwhile but if there is interest in more config options then it starts to make more sense. Ideally a clear set of design goals can be agreed upon now, before it gets too far into the coding period which begins at the end of the month. Peter

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Please don't squeeze!
by Warren Weckesser May 29, 2019

May 29, 2019

Hey fellow SciPy developers, I was just looking into this question on stackoverflow: https://stackoverflow.com/questions/56366443/how-to-let-stats-multivariate-… The question doesn't show a concrete example of the issue, but, as I say in a comment there, I think I know what the problem is. The problem was also reported in an issue on github: https://github.com/scipy/scipy/issues/7689 Here's an example: In [28]: from scipy.stats import multivariate_normal In [29]: mean = [0] In [30]: cov = [[1]] In [31]: multivariate_normal.rvs(mean, cov, size=3) # Result has shape (3,) Out[31]: array([ 0.98481295, -0.64502293, 2.4408484 ]) In [32]: multivariate_normal.rvs(mean, cov, size=2) # Result has shape (2,) Out[32]: array([ 0.48435016, -0.295663 ]) In [33]: multivariate_normal.rvs(mean, cov, size=1) # Result is a scalar! Out[33]: 1.9722388696940718 In [34]: type(_) Out[34]: numpy.float64 When we use `size=N` and `N > 1`, the result has shape (N,). But when we use `size=1`, the result is a scalar with shape (). For consistency, the result in that case should be an array with shape (1,). The problem is the *indiscriminate squeeze* applied to the result before returning. This use of squeeze is almost always the wrong thing to do (IMHO, IME, etc.). And in this case, it is inconsistent with `numpy.random.multvariate_normal`, which, when an explicit `size` is requested, always returns an array. If `mean` has shape (D,) and `cov` has shape (D, D), and size is N, `numpy.random.multivariate_normal` returns an array with shape (D, N), even if D or N is 1. (If `size` is None, the return value has shape (D,). This handling of `size=None` is also consistent throughout `numpy.random`.) This is how `scipy.stats.multivariate_normal` (along with the other multivariate distributions) should behave. I understand the motivation behind the use of `squeeze`. It seems that, if the mean and covariance matrix arguments indicate that the distribution is actually univariate, the caller probably doesn't need the trivial dimension to be included in the output arrays, so let's do the user a favor and remove it. But this creates an issue for the user that *wants* the output shape to remain consistent with the input shapes. I don't recall exactly where, but I know we've had issues with indiscriminate squeezing in the past. So if you've written code that does it, you're not alone, and from a certain perspective it can seem like the right thing to do. But in fact, the problems that it causes far outweigh the few times where the attempt at convenience pays off (IMHO, IME, etc.). We already have an issue for this on github, which is where we should discuss how to fix the multivariate distributions. I'm sending this email to the scipy-dev mailing list in an attempt to raise awareness of this occasionally recurring problem. `squeeze` is almost never the right thing to do (IMHO, IME, etc.), so please, don't squeeze! Warren

1 0

Tidelift agreement for SciPy
by Ralf Gommers May 29, 2019

May 29, 2019

Hi all, On behalf of the SciPy Steering Council I'm happy to announce that we (the SciPy project) now have signed up to the agreement between Tidelift and NumFOCUS. The summary of the agreement is: Tidelift will pay SciPy a minimum of $2500/month until Oct 2020, and SciPy will do the following: - provide a documented way to disclose security vulnerabilities, and respond to disclosures in a timely manner - deal with any licensing issues in a timely manner - write good release notes, and clarify our advice to users on what releases to use - some one-time things like getting our metadata into the Tidelift system, and acknowledging Tidelift as one of our funders on the website This blog gives a nice overview: https://blog.tidelift.com/how-to-start-earning-money-for-your-open-source-p… . Note that it seems to us that this is a quite modest amount of work that we will be able to do with volunteer resources. A lot of it we do anyway - this is a nice feature of Tidelift's business model, in a way they promise their customers that we will keep doing what we're doing, add some valuable things like unified dependency reporting around it, and pass on some of the benefits to the projects (or to individual maintainers for other projects). We haven't determined what to do with the funds yet, but there's lots of things that could be done (organize in-person dev meetings, perhaps fund some work on hairy problems that no one seems to want to solve for free, etc.) - to be determined in the future. The Tidelift model was discussed on th numpy-discussion list back in September ( https://mail.python.org/pipermail/numpy-discussion/2018-September/078736.ht…) but at that point there was no "project wide" solution and the "pay some individuals" model had some issues. Letting all the funding flow into the SciPy account at NumFOCUS nicely solves this. Some PRs that address licensing and vulnerability disclosure issues will follow shortly. Cheers, Ralf

1 0

NumPy 1.16.4 released.
by Charles R Harris May 28, 2019

May 28, 2019

Charles R Harris <charlesr.harris(a)gmail.com> Apr 21, 2019, 8:39 PM to numpy-discussion, SciPy, SciPy-User, bcc: python-announce-list Hi All, On behalf of the NumPy team I am pleased to announce the release of NumPy 1.16.4 which contains several fixes for newly reported bugs. The Python versions supported in this release are 2.7 and 3.5-3.7. Downstream developers building this release should use Cython >= 0.29.2 and, if using OpenBLAS, OpenBLAS > v0.3.7. Wheels for this release can be downloaded from PyPI <https://pypi.org/project/numpy/1.16.4/>, source archives and release notes are available from Github <https://github.com/numpy/numpy/releases/tag/v1.16.4>. If you are installing using pip, you may encounter a problem with older installed versions of NumPy that pip did not delete becoming mixed with the current version, resulting in an ``ImportError``. That problem is particularly common on Debian derived distributions due to a modified pip. The fix is to make sure all previous NumPy versions installed by pip have been removed. See #12736 <https://github.com/numpy/numpy/issues/12736> for discussion of the issue. *Contributors* A total of 10 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Charles Harris - Eric Wieser - Dennis Zollo + - Hunter Damron + - Jingbei Li + - Kevin Sheppard - Matti Picus - Nicola Soranzo + - Sebastian Berg - Tyler Reddy *Pull requests merged* A total of 16 pull requests were merged for this release. - gh-13392: BUG: Some PyPy versions lack PyStructSequence_InitType2. - gh-13394: MAINT, DEP: Fix deprecated ``assertEquals()`` - gh-13396: BUG: Fix structured_to_unstructured on single-field types (backport) - gh-13549: BLD: Make CI pass again with pytest 4.5 - gh-13552: TST: Register markers in conftest.py. - gh-13559: BUG: Removes ValueError for empty kwargs in arraymultiter_new - gh-13560: BUG: Add TypeError to accepted exceptions in crackfortran. - gh-13561: BUG: Handle subarrays in descr_to_dtype - gh-13562: BUG: Protect generators from log(0.0) - gh-13563: BUG: Always return views from structured_to_unstructured when... - gh-13564: BUG: Catch stderr when checking compiler version - gh-13565: BUG: longdouble(int) does not work - gh-13587: BUG: distutils/system_info.py fix missing subprocess import (#13523) - gh-13620: BUG,DEP: Fix writeable flag setting for arrays without base - gh-13641: MAINT: Prepare for the 1.16.4 release. - gh-13644: BUG: special case object arrays when printing rel-, abs-error Cheers, Charles Harris

1 0

scipy.interpolate - other use cases
by Dieter Werthmüller May 28, 2019

May 28, 2019

Dear Devs, I have a particular issue with scipy.interpolate for which I would like to know: - Is there already a better way to do it which I miss? - Is there interest to add this functionality to SciPy? or - Is there interest to extend the functionality of the existing fct? - None of the above or other ideas/comments? My example deals with scipy.interpolate.RegularGridInterpolator; however, it probably is the same for many of the interpolators. The usage of it in, e.g., 2D is func = RegularGridInterpolator((x, y), data) func(pts) where `x` and `y` define the regular grid on which `data` is defined, and we interpolate it at `pts`-coordinates. This is ideal if you want to interpolate the same data for different points again and again. However, there are other use cases. Imagine you have a coarse grid, defined by `x`, `y`, and a fine grid, defined by the `pts`-coordinates. Both grids are regular grids, but not equidistant. Now we want to interpolate a changing `data` from the coarse grid to the fine grid. The current implementation is not good for that, as you have to create a new RegularGridInterpolator-instance for each new data-set, which involves the calculation of the indices and weights every time, even though they remain the same. So in this case, the implementation should instead look like: func = RegularGridInterpolator((x, y), pts) func(data) hence, `pts` and `data` interchanged. In this way, the required indices and weights can all be calculated during the initiation, which will significantly speed-up the interpolation. And `func` can subsequently be called relatively cheaply in, for instance, a loop which changes the data every time, but not the coordinates. This behaviour can be obtained by simply moving things between the `__init__` and `__call__` functions in `RegularGridInterpolator`, which could probably be done as a switch triggered by a new parameter. Any thoughts? Dieter

3 3

Schumaker quadratic spline
by Hansen, Clifford W May 24, 2019

May 24, 2019

Jumping in the middle of this discussion: I wrote the original code (Matlab) that Mark is working to merge into our python library pvlib-python. I'd rather see the spline function find a home in scipy if that's appropriate; the functions that use the spline are specific to a photovoltaic model. The Schumaker spline preserves both local monotonicity and convexity, and so it's different than the shape-preserving cubic spline in Pchip or Akima (it appears those splines preserve monotonicity but not convexity). The knot selection in Schumaker is different from that typically used for B-splines. I can look at BSpline and PPoly to see if those functions can be leveraged in Schumaker's algorithm. Didn't know about the R package, which should be an excellent guide for the python implementation. Cliff > On Fri, May 10, 2019 at 12:27 AM Evgeni Burovski < > evgeny.burovskiy at gmail.com> wrote: > >> Hi Mark, >> >> The docs you linked reference https://epubs.siam.org/doi/10.1137/0720057 >> which is gated, so only an impression: this seems to construct a >> shape-preserving piecewise quadratic interpolant. We have two >> shape-preserving cubic interpolants, PCHIP and Akima. Do you have an >> overview of how this one compares to them? >> >> Off the cuff, I guess we can add a shape-preserving quadratic, too. This >> should be based on either BSpline or PPoly, depending on the basis the >> construction is in. >> (My impression is that it's the latter in the R package). >> The API should mirror that of Akima1DInterpolator. >> >>

1 0

Idea discussion for GSOD'19
by shivam pathak May 23, 2019

May 23, 2019

Hi sir, I am Shivam Pathak, Computer Science Undergrad at AIT Pune, India. I am interested in contributing to Scipy's Documentation and has read the idea page. Where can I start now to get involved?

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ANN: SciPy 1.3.0
by Tyler Reddy May 20, 2019

May 20, 2019

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA256 Hi all, On behalf of the SciPy development team I'm pleased to announce the release of SciPy 1.3.0. Sources and binary wheels can be found at: https://pypi.org/project/scipy/ and at: https://github.com/scipy/scipy/releases/tag/v1.3.0 One of a few ways to install this release with pip: pip install scipy==1.3.0 ========================== SciPy 1.3.0 Release Notes ========================== 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.optimize` changes --------------------------------- The default method for ``linprog`` is now ``'interior-point'``. The method's robustness and speed come at a cost: solutions may not be accurate to machine precision or correspond with a vertex of the polytope defined by the constraints. To revert to the original simplex method, include the argument ``method='simplex'``. `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 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Schumaker quadratic spline
by Mark Mikofski May 17, 2019

May 17, 2019

Hi All, Sorry for this unresearched and inexperienced question. Does anyone have any experience with the "Schumaker quadratic spline" for 1-D interpolation? Is it similar enough to the BSpline already in SciPy? I have some source code that I can contribute if there's interest. As far as references, a google search turned up an R function: * https://cran.r-project.org/web/packages/schumaker/vignettes/schumaker.html -- Mark Mikofski, PhD (2005) *Fiat Lux*

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Prerequisites for contributing?
by Jonathan Conroy May 15, 2019

May 15, 2019

Hi all, I'm Jonathan, a rising sophomore studying math and computer science. I'm hoping to start contributing to open source software over the summer and SciPy seems like a really interesting library, but I'm worried I don't have the experience to make meaningful contributions. I'm looking for advice on whether or not I have the background to contribute. In terms of coursework, I've taken Data Structures & Algorithms and Machine Architecture, and I've had calculus, linear algebra, and a bit of real analysis. I'll be taking a few machine learning courses next year (and will be using SciPy in them), but as of right now I haven't used SciPy in any large projects. That being said, I have a bunch of time on my hands and would be eager to spend it learning the codebase this summer. Do you think contributing to SciPy would be doable/worthwhile for me, or would you recommend looking for other projects and returning to SciPy later on? Are there any specific SciPy modules that would be more accessable than others? Any advice is appreciated! Thanks for your time, Jonathan Conroy

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