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MAINT: master updates after release
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* synchronize 1.2.0 release notes on
master branch now that 1.2.0 has been
released

* update pavement.py for Python 3.x
compatibility & minor bug fix
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@tylerjereddy
tylerjereddy committed Dec 18, 2018
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120 changes: 67 additions & 53 deletions doc/release/1.2.0-notes.rst
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Expand Up @@ -2,8 +2,6 @@
SciPy 1.2.0 Release Notes
==========================

.. note:: Scipy 1.2.0 is not released yet!

.. contents::

SciPy 1.2.0 is the culmination of 6 months of hard work. It contains
Expand All @@ -19,12 +17,16 @@ Our development attention will now shift to bug-fix releases on the

This release requires Python 2.7 or 3.4+ and NumPy 1.8.2 or greater.

.. note:: This will be the last SciPy release to support Python 2.7.
Consequently, the 1.2.x series will be a long term support (LTS)
release; we will backport bug fixes until 1 Jan 2020.

For running on PyPy, PyPy3 6.0+ and NumPy 1.15.0 are required.

Highlights of this release
--------------------------

- 1-D root finding improvements with a new solver, ``toms748``, and a new
- 1-D root finding improvements with a new solver, ``toms748``, and a new
unified interface, ``root_scalar``
- New ``dual_annealing`` optimization method that combines stochastic and
local deterministic searching
Expand All @@ -45,7 +47,7 @@ Proper spline coefficient calculations have been added for the ``mirror``,
`scipy.fftpack` improvements
--------------------------------

DCT-IV, DST-IV, DCT-I, and DST-I orthonormalization are now supported in
DCT-IV, DST-IV, DCT-I, and DST-I orthonormalization are now supported in
`scipy.fftpack`.

`scipy.interpolate` improvements
Expand All @@ -67,11 +69,11 @@ The function ``softmax`` was added to `scipy.special`.
`scipy.optimize` improvements
-----------------------------

The one-dimensional nonlinear solvers have been given a unified interface
`scipy.optimize.root_scalar`, similar to the `scipy.optimize.root` interface
The one-dimensional nonlinear solvers have been given a unified interface
`scipy.optimize.root_scalar`, similar to the `scipy.optimize.root` interface
for multi-dimensional solvers. ``scipy.optimize.root_scalar(f, bracket=[a ,b],
method="brenth")`` is equivalent to ``scipy.optimize.brenth(f, a ,b)``. If no
``method`` is specified, an appropriate one will be selected based upon the
``method`` is specified, an appropriate one will be selected based upon the
bracket and the number of derivatives available.

The so-called Algorithm 748 of Alefeld, Potra and Shi for root-finding within
Expand All @@ -81,27 +83,27 @@ of approximately 1.65 (for sufficiently well-behaved functions.)

``differential_evolution`` now has the ``updating`` and ``workers`` keywords.
The first chooses between continuous updating of the best solution vector (the
default), or once per generation. Continuous updating can lead to faster
default), or once per generation. Continuous updating can lead to faster
convergence. The ``workers`` keyword accepts an ``int`` or map-like callable,
and parallelises the solver (having the side effect of updating once per
and parallelises the solver (having the side effect of updating once per
generation). Supplying an ``int`` evaluates the trial solutions in N parallel
parts. Supplying a map-like callable allows other parallelisation approaches
parts. Supplying a map-like callable allows other parallelisation approaches
(such as ``mpi4py``, or ``joblib``) to be used.

``dual_annealing`` (and ``shgo`` below) is a powerful new general purpose
global optizimation (GO) algorithm. ``dual_annealing`` uses two annealing
processes to accelerate the convergence towards the global minimum of an
objective mathematical function. The first annealing process controls the
stochastic Markov chain searching and the second annealing process controls the
deterministic minimization. So, dual annealing is a hybrid method that takes
``dual_annealing`` (and ``shgo`` below) is a powerful new general purpose
global optizimation (GO) algorithm. ``dual_annealing`` uses two annealing
processes to accelerate the convergence towards the global minimum of an
objective mathematical function. The first annealing process controls the
stochastic Markov chain searching and the second annealing process controls the
deterministic minimization. So, dual annealing is a hybrid method that takes
advantage of stochastic and local deterministic searching in an efficient way.

``shgo`` (simplicial homology global optimization) is a similar algorithm
appropriate for solving black box and derivative free optimization (DFO)
problems. The algorithm generally converges to the global solution in finite
time. The convergence holds for non-linear inequality and
equality constraints. In addition to returning a global minimum, the
algorithm also returns any other global and local minima found after every
appropriate for solving black box and derivative free optimization (DFO)
problems. The algorithm generally converges to the global solution in finite
time. The convergence holds for non-linear inequality and
equality constraints. In addition to returning a global minimum, the
algorithm also returns any other global and local minima found after every
iteration. This makes it useful for exploring the solutions in a domain.

`scipy.optimize.newton` can now accept a scalar or an array
Expand All @@ -113,18 +115,18 @@ be used on multiple threads.
---------------------------

Digital filter design functions now include a parameter to specify the sampling
rate. Previously, digital filters could only be specified using normalized
frequency, but different functions used different scales (e.g. 0 to 1 for
``butter`` vs 0 to π for ``freqz``), leading to errors and confusion. With
rate. Previously, digital filters could only be specified using normalized
frequency, but different functions used different scales (e.g. 0 to 1 for
``butter`` vs 0 to π for ``freqz``), leading to errors and confusion. With
the ``fs`` parameter, ordinary frequencies can now be entered directly into
functions, with the normalization handled internally.

``find_peaks`` and related functions no longer raise an exception if the
``find_peaks`` and related functions no longer raise an exception if the
properties of a peak have unexpected values (e.g. a prominence of 0). A
``PeakPropertyWarning`` is given instead.

The new keyword argument ``plateau_size`` was added to ``find_peaks``.
``plateau_size`` may be used to select peaks based on the length of the
The new keyword argument ``plateau_size`` was added to ``find_peaks``.
``plateau_size`` may be used to select peaks based on the length of the
flat top of a peak.

``welch()`` and ``csd()`` methods in `scipy.signal` now support calculation
Expand All @@ -140,12 +142,12 @@ conversions is now improved.

The issue where SuperLU or UMFPACK solvers crashed on matrices with
non-canonical format in `scipy.sparse.linalg` was fixed. The solver wrapper
canonicalizes the matrix if necessary before calling the SuperLU or UMFPACK
solver.
canonicalizes the matrix if necessary before calling the SuperLU or UMFPACK
solver.

The ``largest`` option of `scipy.sparse.linalg.lobpcg()` was fixed to have
a correct (and expected) behavior. The order of the eigenvalues was made
consistent with the ARPACK solver (``eigs()``), i.e. ascending for the
a correct (and expected) behavior. The order of the eigenvalues was made
consistent with the ARPACK solver (``eigs()``), i.e. ascending for the
smallest eigenvalues, and descending for the largest eigenvalues.

The `scipy.sparse.random` function is now faster and also supports integer and
Expand All @@ -155,20 +157,20 @@ complex values by passing the appropriate value to the ``dtype`` argument.
----------------------------

The function `scipy.spatial.distance.jaccard` was modified to return 0 instead
of ``np.nan`` when two all-zero vectors are compared.
of ``np.nan`` when two all-zero vectors are compared.

Support for the Jensen Shannon distance, the square-root of the divergence, has
been added under `scipy.spatial.distance.jensenshannon`

An optional keyword was added to the function
`scipy.spatial.cKDTree.query_ball_point()` to sort or not sort the returned
An optional keyword was added to the function
`scipy.spatial.cKDTree.query_ball_point()` to sort or not sort the returned
indices. Not sorting the indices can speed up calls.

A new category of quaternion-based transformations are available in
`scipy.spatial.transform`, including spherical linear interpolation of
rotations (``Slerp``), conversions to and from quaternions, Euler angles,
and general rotation and inversion capabilities
(`spatial.transform.Rotation`), and uniform random sampling of 3D
and general rotation and inversion capabilities
(`spatial.transform.Rotation`), and uniform random sampling of 3D
rotations (`spatial.transform.Rotation.random`).

`scipy.stats` improvements
Expand All @@ -182,10 +184,10 @@ values.
Added a general method to sample random variates based on the density only, in
the new function ``rvs_ratio_uniforms``.

The Yule-Simon distribution (``yulesimon``) was added -- this is a new
The Yule-Simon distribution (``yulesimon``) was added -- this is a new
discrete probability distribution.

``stats`` and ``mstats`` now have access to a new regression method,
``stats`` and ``mstats`` now have access to a new regression method,
``siegelslopes``, a robust linear regression algorithm

`scipy.stats.gaussian_kde` now has the ability to deal with weighted samples,
Expand All @@ -200,8 +202,8 @@ and ``mstats``
`scipy.linalg` improvements
--------------------------

`scipy.linalg.lapack` now exposes the LAPACK routines using the Rectangular
Full Packed storage (RFP) for upper triangular, lower triangular, symmetric,
`scipy.linalg.lapack` now exposes the LAPACK routines using the Rectangular
Full Packed storage (RFP) for upper triangular, lower triangular, symmetric,
or Hermitian matrices; the upper trapezoidal fat matrix RZ decomposition
routines are now available as well.

Expand All @@ -221,34 +223,34 @@ The function ``scipy.linalg.subspace_angles(A, B)`` now gives correct
results for all angles. Before this, the function only returned
correct values for those angles which were greater than pi/4.

Support for the Bento build system has been removed. Bento has not been
Support for the Bento build system has been removed. Bento has not been
maintained for several years, and did not have good Python 3 or wheel support,
hence it was time to remove it.

The required signature of `scipy.optimize.lingprog` ``method=simplex``
callback function has changed. Before iteration begins, the simplex solver
The required signature of `scipy.optimize.lingprog` ``method=simplex``
callback function has changed. Before iteration begins, the simplex solver
first converts the problem into a standard form that does not, in general,
have the same variables or constraints
as the problem defined by the user. Previously, the simplex solver would pass a
user-specified callback function several separate arguments, such as the
current solution vector ``xk``, corresponding to this standard form problem.
Unfortunately, the relationship between the standard form problem and the
user-defined problem was not documented, limiting the utility of the
user-specified callback function several separate arguments, such as the
current solution vector ``xk``, corresponding to this standard form problem.
Unfortunately, the relationship between the standard form problem and the
user-defined problem was not documented, limiting the utility of the
information passed to the callback function.

In addition to numerous bug fix changes, the simplex solver now passes a
In addition to numerous bug fix changes, the simplex solver now passes a
user-specified callback function a single ``OptimizeResult`` object containing
information that corresponds directly to the user-defined problem. In future
releases, this ``OptimizeResult`` object may be expanded to include additional
information, such as variables corresponding to the standard-form problem and
information concerning the relationship between the standard-form and
information, such as variables corresponding to the standard-form problem and
information concerning the relationship between the standard-form and
user-defined problems.

The implementation of `scipy.sparse.random` has changed, and this affects the
numerical values returned for both ``sparse.random`` and ``sparse.rand`` for
The implementation of `scipy.sparse.random` has changed, and this affects the
numerical values returned for both ``sparse.random`` and ``sparse.rand`` for
some matrix shapes and a given seed.

`scipy.optimize.newton` will no longer use Halley's method in cases where it
`scipy.optimize.newton` will no longer use Halley's method in cases where it
negatively impacts convergence

Other changes
Expand Down Expand Up @@ -294,6 +296,7 @@ Authors
* emmi474 +
* Stefan Endres +
* Thomas Etherington +
* Piotr Figiel
* Alex Fikl +
* fo40225 +
* Joseph Fox-Rabinovitz
Expand Down Expand Up @@ -400,6 +403,9 @@ This list of names is automatically generated, and may not be fully complete.
Issues closed for 1.2.0
-----------------------

* `#9520 <https://github.com/scipy/scipy/issues/9520>`__: signal.correlate with method='fft' doesn't benefit from long...
* `#9547 <https://github.com/scipy/scipy/issues/9547>`__: signature of dual_annealing doesn't match other optimizers
* `#9540 <https://github.com/scipy/scipy/issues/9540>`__: SciPy v1.2.0rc1 cannot be imported on Python 2.7.15
* `#1240 <https://github.com/scipy/scipy/issues/1240>`__: Allowing multithreaded use of minpack through scipy.optimize...
* `#1432 <https://github.com/scipy/scipy/issues/1432>`__: scipy.stats.mode extremely slow (Trac #905)
* `#3372 <https://github.com/scipy/scipy/issues/3372>`__: Please add Sphinx search field to online scipy html docs
Expand Down Expand Up @@ -461,6 +467,14 @@ Issues closed for 1.2.0
Pull requests for 1.2.0
-----------------------

* `#9526 <https://github.com/scipy/scipy/pull/9526>`__: TST: relax precision requirements in signal.correlate tests
* `#9507 <https://github.com/scipy/scipy/pull/9507>`__: CI: MAINT: Skip a ckdtree test on pypy
* `#9512 <https://github.com/scipy/scipy/pull/9512>`__: TST: test_random_sampling 32-bit handling
* `#9494 <https://github.com/scipy/scipy/pull/9494>`__: TST: test_kolmogorov xfail 32-bit
* `#9486 <https://github.com/scipy/scipy/pull/9486>`__: BUG: fix sparse random int handling
* `#9550 <https://github.com/scipy/scipy/pull/9550>`__: BUG: scipy/_lib/_numpy_compat: get_randint
* `#9549 <https://github.com/scipy/scipy/pull/9549>`__: MAINT: make dual_annealing signature match other optimizers
* `#9541 <https://github.com/scipy/scipy/pull/9541>`__: BUG: fix SyntaxError due to non-ascii character on Python 2.7
* `#7352 <https://github.com/scipy/scipy/pull/7352>`__: ENH: add Brunner Munzel test to scipy.stats.
* `#7373 <https://github.com/scipy/scipy/pull/7373>`__: BUG: Jaccard distance for all-zero arrays would return np.nan
* `#7374 <https://github.com/scipy/scipy/pull/7374>`__: ENH: Add PDF, CDF and parameter estimation for Stable Distributions
Expand Down
10 changes: 5 additions & 5 deletions pavement.py
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Original file line number Diff line number Diff line change
Expand Up @@ -74,7 +74,7 @@
from paver.tasks import VERSION as _PVER
if not _PVER >= '1.0':
raise RuntimeError("paver version >= 1.0 required (was %s)" % _PVER)
except ImportError, e:
except (ImportError, e):
raise RuntimeError("paver version >= 1.0 required")

import paver
Expand Down Expand Up @@ -319,7 +319,7 @@ def sdist():
sh('git submodule update')

# Fix file permissions
sh('chmod a+rX -R *')
sh('chmod -R a+rX *')

# To be sure to bypass paver when building sdist... paver + scipy.distutils
# do not play well together.
Expand Down Expand Up @@ -658,7 +658,7 @@ def compute_md5(idirs):
released = paver.path.path(idirs).listdir()
checksums = []
for f in sorted(released):
m = md5(open(f, 'r').read())
m = md5(open(f, 'rb').read())
checksums.append('%s %s' % (m.hexdigest(), os.path.basename(f)))

return checksums
Expand All @@ -669,7 +669,7 @@ def compute_sha256(idirs):
released = paver.path.path(idirs).listdir()
checksums = []
for f in sorted(released):
m = sha256(open(f, 'r').read())
m = sha256(open(f, 'rb').read())
checksums.append('%s %s' % (m.hexdigest(), os.path.basename(f)))

return checksums
Expand Down Expand Up @@ -716,7 +716,7 @@ def write_log_task(filename='Changelog'):
['git', 'log', '%s..%s' % (LOG_START, LOG_END)],
stdout=subprocess.PIPE)

out = st.communicate()[0]
out = st.communicate()[0].decode()
a = open(filename, 'w')
a.writelines(out)
a.close()
Expand Down

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