{"action":"closed","number":46,"pull_request":{"url":"https://api.github.com/repos/GiulioRossetti/cdlib/pulls/46","id":286192626,"node_id":"MDExOlB1bGxSZXF1ZXN0Mjg2MTkyNjI2","html_url":"https://github.com/GiulioRossetti/cdlib/pull/46","diff_url":"https://github.com/GiulioRossetti/cdlib/pull/46.diff","patch_url":"https://github.com/GiulioRossetti/cdlib/pull/46.patch","issue_url":"https://api.github.com/repos/GiulioRossetti/cdlib/issues/46","number":46,"state":"closed","locked":false,"title":"Update scipy to 1.3.0","user":{"login":"pyup-bot","id":16239342,"node_id":"MDQ6VXNlcjE2MjM5MzQy","avatar_url":"https://avatars0.githubusercontent.com/u/16239342?v=4","gravatar_id":"","url":"https://api.github.com/users/pyup-bot","html_url":"https://github.com/pyup-bot","followers_url":"https://api.github.com/users/pyup-bot/followers","following_url":"https://api.github.com/users/pyup-bot/following{/other_user}","gists_url":"https://api.github.com/users/pyup-bot/gists{/gist_id}","starred_url":"https://api.github.com/users/pyup-bot/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/pyup-bot/subscriptions","organizations_url":"https://api.github.com/users/pyup-bot/orgs","repos_url":"https://api.github.com/users/pyup-bot/repos","events_url":"https://api.github.com/users/pyup-bot/events{/privacy}","received_events_url":"https://api.github.com/users/pyup-bot/received_events","type":"User","site_admin":false},"body":"\nThis PR updates [scipy](https://pypi.org/project/scipy) from **1.2.1** to **1.3.0**.\n\n\n\n<details>\n  <summary>Changelog</summary>\n  \n  \n   ### 1.3.0\n   ```\n   many new features, numerous bug-fixes, improved test coverage and better\ndocumentation. There have been some API changes\nin this release, which are documented below. All users are encouraged to\nupgrade to this release, as there are a large number of bug-fixes and\noptimizations. Before upgrading, we recommend that users check that\ntheir own code does not use deprecated SciPy functionality (to do so,\nrun your code with ``python -Wd`` and check for ``DeprecationWarning`` s).\nOur development attention will now shift to bug-fix releases on the\n1.3.x branch, and on adding new features on the master branch.\n\nThis release requires Python 3.5+ and NumPy 1.13.3 or greater.\n\nFor running on PyPy, PyPy3 6.0+ and NumPy 1.15.0 are required.\n\nHighlights of this release\n--------------------------\n\n- Three new ``stats`` functions, a rewrite of ``pearsonr``, and an exact\n  computation of the Kolmogorov-Smirnov two-sample test\n- A new Cython API for bounded scalar-function root-finders in `scipy.optimize`\n- Substantial ``CSR`` and ``CSC`` sparse matrix indexing performance\n  improvements\n- Added support for interpolation of rotations with continuous angular\n  rate and acceleration in ``RotationSpline``\n\n\nNew features\n============\n\n`scipy.interpolate` improvements\n--------------------------------\n\nA new class ``CubicHermiteSpline`` is introduced. It is a piecewise-cubic \ninterpolator which matches observed values and first derivatives. Existing \ncubic interpolators ``CubicSpline``, ``PchipInterpolator`` and \n``Akima1DInterpolator`` were made subclasses of ``CubicHermiteSpline``.\n\n`scipy.io` improvements\n-----------------------\n\nFor the Attribute-Relation File Format (ARFF) `scipy.io.arff.loadarff` \nnow supports relational attributes.\n\n`scipy.io.mmread` can now parse Matrix Market format files with empty lines.\n\n`scipy.linalg` improvements\n---------------------------\n\nAdded wrappers for ``?syconv`` routines, which convert a symmetric matrix \ngiven by a triangular matrix factorization into two matrices and vice versa.\n\n`scipy.linalg.clarkson_woodruff_transform` now uses an algorithm that leverages\nsparsity. This may provide a 60-90 percent speedup for dense input matrices.\nTruly sparse input matrices should also benefit from the improved sketch\nalgorithm, which now correctly runs in ``O(nnz(A))`` time.\n\nAdded new functions to calculate symmetric Fiedler matrices and\nFiedler companion matrices, named `scipy.linalg.fiedler` and \n`scipy.linalg.fiedler_companion`, respectively. These may be used\nfor root finding.\n\n`scipy.ndimage` improvements\n----------------------------\n\nGaussian filter performances may improve by an order of magnitude in\nsome cases, thanks to removal of a dependence on ``np.polynomial``. This\nmay impact `scipy.ndimage.gaussian_filter` for example.\n\n`scipy.optimize` improvements\n-----------------------------\n\nThe `scipy.optimize.brute` minimizer obtained a new keyword ``workers``, which\ncan be used to parallelize computation.\n\nA Cython API for bounded scalar-function root-finders in `scipy.optimize`\nis available in a new module `scipy.optimize.cython_optimize` via ``cimport``.\nThis API may be used with ``nogil`` and ``prange`` to loop \nover an array of function arguments to solve for an array of roots more \nquickly than with pure Python.\n\n``&#39;interior-point&#39;`` is now the default method for ``linprog``, and \n``&#39;interior-point&#39;`` now uses SuiteSparse for sparse problems when the \nrequired scikits  (scikit-umfpack and scikit-sparse) are available. \nOn benchmark problems (gh-10026), execution time reductions by factors of 2-3 \nwere typical. Also, a new ``method=&#39;revised simplex&#39;`` has been added. \nIt is not as fast or robust as ``method=&#39;interior-point&#39;``, but it is a faster,\nmore robust, and equally accurate substitute for the legacy \n``method=&#39;simplex&#39;``.\n\n``differential_evolution`` can now use a ``Bounds`` class to specify the\nbounds for the optimizing argument of a function.\n\n`scipy.optimize.dual_annealing` performance improvements related to\nvectorisation of some internal code.\n\n`scipy.signal` improvements\n---------------------------\n\nTwo additional methods of discretization are now supported by \n`scipy.signal.cont2discrete`: ``impulse`` and ``foh``.\n\n`scipy.signal.firls` now uses faster solvers\n\n`scipy.signal.detrend` now has a lower physical memory footprint in some\ncases, which may be leveraged using the new ``overwrite_data`` keyword argument\n\n`scipy.signal.firwin` ``pass_zero`` argument now accepts new string arguments\nthat allow specification of the desired filter type: ``&#39;bandpass&#39;``,\n``&#39;lowpass&#39;``, ``&#39;highpass&#39;``, and ``&#39;bandstop&#39;``\n\n`scipy.signal.sosfilt` may have improved performance due to lower retention\nof the global interpreter lock (GIL) in algorithm\n\n`scipy.sparse` improvements\n---------------------------\n\nA new keyword was added to ``csgraph.dijsktra`` that \nallows users to query the shortest path to ANY of the passed in indices,\nas opposed to the shortest path to EVERY passed index.\n\n`scipy.sparse.linalg.lsmr` performance has been improved by roughly 10 percent\non large problems\n\nImproved performance and reduced physical memory footprint of the algorithm\nused by `scipy.sparse.linalg.lobpcg`\n\n``CSR`` and ``CSC`` sparse matrix fancy indexing performance has been\nimproved substantially\n\n`scipy.spatial` improvements\n----------------------------\n\n`scipy.spatial.ConvexHull` now has a ``good`` attribute that can be used \nalongsize the ``QGn`` Qhull options to determine which external facets of a \nconvex hull are visible from an external query point.\n\n`scipy.spatial.cKDTree.query_ball_point` has been modernized to use some newer \nCython features, including GIL handling and exception translation. An issue \nwith ``return_sorted=True`` and scalar queries was fixed, and a new mode named \n``return_length`` was added. ``return_length`` only computes the length of the \nreturned indices list instead of allocating the array every time.\n\n`scipy.spatial.transform.RotationSpline` has been added to enable interpolation\nof rotations with continuous angular rates and acceleration\n\n`scipy.stats` improvements\n--------------------------\n\nAdded a new function to compute the Epps-Singleton test statistic,\n`scipy.stats.epps_singleton_2samp`, which can be applied to continuous and\ndiscrete distributions.\n\nNew functions `scipy.stats.median_absolute_deviation` and `scipy.stats.gstd`\n(geometric standard deviation) were added. The `scipy.stats.combine_pvalues` \nmethod now supports ``pearson``,  ``tippett`` and ``mudholkar_george`` pvalue \ncombination methods.\n\nThe `scipy.stats.ortho_group` and `scipy.stats.special_ortho_group` \n``rvs(dim)`` functions&#39; algorithms were updated from a ``O(dim^4)`` \nimplementation to a ``O(dim^3)`` which gives large speed improvements \nfor ``dim&gt;100``.\n\nA rewrite of `scipy.stats.pearsonr` to use a more robust algorithm,\nprovide meaningful exceptions and warnings on potentially pathological input,\nand fix at least five separate reported issues in the original implementation.\n\nImproved the precision of ``hypergeom.logcdf`` and ``hypergeom.logsf``.\n\nAdded exact computation for Kolmogorov-Smirnov (KS) two-sample test, replacing\nthe previously approximate computation for the two-sided test `stats.ks_2samp`.\nAlso added a one-sided, two-sample KS test, and a keyword ``alternative`` to \n`stats.ks_2samp`.\n\nBackwards incompatible changes\n==============================\n\n`scipy.interpolate` changes\n---------------------------\n\nFunctions from ``scipy.interpolate`` (``spleval``, ``spline``, ``splmake``,\nand ``spltopp``) and functions from ``scipy.misc`` (``bytescale``,\n``fromimage``, ``imfilter``, ``imread``, ``imresize``, ``imrotate``,\n``imsave``, ``imshow``, ``toimage``) have been removed. The former set has \nbeen deprecated since v0.19.0 and the latter has been deprecated since v1.0.0.\nSimilarly, aliases from ``scipy.misc`` (``comb``, ``factorial``,\n``factorial2``, ``factorialk``, ``logsumexp``, ``pade``, ``info``, ``source``,\n``who``) which have been deprecated since v1.0.0 are removed. \n`SciPy documentation for\n   ```\n   \n  \n  \n  \n  \n  \n</details>\n\n\n \n\n<details>\n  <summary>Links</summary>\n  \n  - PyPI: https://pypi.org/project/scipy\n  - Changelog: https://pyup.io/changelogs/scipy/\n  - Repo: https://github.com/scipy/scipy/releases\n  - Homepage: 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