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