- 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 `__
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