- Update to 1.3.0
  + 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.
- Python 2 is no longer supported

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