Hi All,
I plan on releasing NumPy 1.16.0rc in a day or two. The preliminary release
notes <https://github.com/numpy/numpy/pull/12590> are up and any
suggestions/corrections/additions are welcome.
Chuck
I believe this was proposed in the past to little enthusiasm,
with the response, "you're using a library; learn its functions".
Nevertheless, given the addition of `choices` to the Python
random module in 3.6, it would be nice to have the *same name*
for parallel functionality in numpy.random.
And given the redundancy of numpy.random.sample, it would be
nice to deprecate it with the intent to reintroduce
the name later, better aligned with Python's usage.
Obviously numpy.random.choice exists for both cases,
so this comment is not about functionality.
And I accept that some will think it is not about anything.
Perhaps it might be at least seen as being about this:
using the same function (`choice`) with a boolean argument
(`replace`) to switch between sampling strategies at least
appears to violate the proposal floated at times on this
list that called for two separate functions in apparently
similar cases. (I am not at all trying to claim that the
argument against flag parameters is definitive; I'm just
mentioning that this viewpoint has already been
promulgated on this list.)
Cheers, Alan Isaac
Dear community,
I started the rewrite of "numpy.pad" [1] some time ago and feel like it
has now left the WIP status. All tests are green and the benchmarks
promise significant improvements for large arrays [2].
However progress on this PR seems to have stalled somewhat. As was
brought up multiple times, a reasonable concern is that introducing
these changes incrementally is preferable to a single PR. This was
partly possible (see [3]) but as explained further down is not trivial
for the remaining changes.
Concerning the suggestion to split the remaining changes I proposed 4
ways forward (copied from comment [4]):
Option 1: Split this PR in smaller PRs with the requirement to pass the
test suite. I expect this will make this a lot more complicated. At some
points I will just have to introduce an overhead of logic to control old
and new code paths at the same time. I just don't see another way
because the two approaches are so different. I'm really not a fan of
creating functions that work with both approaches if this leads to a
weird architecture in the long run. Also while this will split the
review burden into smaller pieces I think it will increase the sum of
changes to review in total.
Option 2: Split this PR in smaller PRs without the requirement to pass
the test suite (mark appropriate tests as xfail). Create a new branch in
numpy's repo just for the rewrite. I could then make my PRs against this
branch without master suffering. I'm thinking of first wiping the slate,
introducing shared functions (for all modes) and then incrementally
introducing each "new" mode with a new PR. This would keep the review
burden for each PR and the rewrite overall small and still allow
benchmarking of all combined changes against master.
Option 3: Squash changes in this PR and continue with a new PR to
maintain clarity as this PR has gotten quite big while I was still
making changes and addressing reviews.
Option 4: Continue this PR.
I myself think option 2 is the most elegant and best compromise between
all concerns. However I can't move forward on my own as this option
requires a maintainer to set up a temporary branch for me to make PRs
against.
So I'd like to kindly ask for your thoughts and guidance.
Best regards,
Lars
[1] https://github.com/numpy/numpy/pull/11358
[2] https://github.com/numpy/numpy/pull/11358#issuecomment-441246090
[3] https://github.com/numpy/numpy/pull/11966
[4] https://github.com/numpy/numpy/pull/11358#issuecomment-441362401
Hi all,
A little while ago I wrote a blog post about the history of us constructing
the roadmap we currently have:
https://rgommers.github.io/2018/10/the-making-of-the-numpy-roadmap/. There
was a request to post it to this list, which seems appropriate. So below it
is in full.
Cheers,
Ralf
NumPy now has a [roadmap](https://www.numpy.org/neps/index.html#roadmap) -
long overdue and a major step forward for the project. We're not done yet
(see [my previous post](
https://rgommers.github.io/2018/10/2018-numfocus-summit---a-summary/) on
this topic) and updating the technical roadmap with new ideas and
priorities should happen regularly. Despite everything having been done in
the open, via minutes of in-person meetings and shared roadmap drafts on
the numpy-discussion mailing list, it turns out that it's not completely
clear to the community and even some maintainers how we got to this point.
So now seems like a good time for a summary.
## Act 1: NumPy sprint at BIDS
During 24-25 May 2018 a number of people - Stéfan van der Walt, Charles
Harris, Stephan Hoyer, Matti Picus, Jarrod Millman, Nathaniel J. Smith, and
Matthew Rocklin - came together at [BIDS](https://bids.berkeley.edu/) in
Berkeley for a NumPy sprint. They had a productive brainstorm, which
resulted in a rough draft of a roadmap posted to the mailing list. The
mailing list thread, [A roadmap for NumPy - longer term planning](
https://mail.python.org/pipermail/numpy-discussion/2018-June/thread.html#78…),
produced more suggestions and after incorporating those a [first pull
request](https://github.com/numpy/numpy/pull/11446) was made.
## Act 2: BoF and sprint at SciPy'18
In order to collect input from as many people in the community as possible,
a BoF ([birds of a feather](
https://en.wikipedia.org/wiki/Birds_of_a_feather_(computing) session) was
held on 12 July 2018 during the SciPy 2018 conference in Austin. Stephan
Hoyer presented an overview of recent NEPs, and Matti Picus presented the
first roadmap draft and led a discussion with (I estimate) 30-40 people in
which a number of new ideas were added. Other maintainers present included
Charles Harris, Tyler Reddy, Ralf Gommers and Stéfan van der Walt; many
other participants were developers of libraries that depend on NumPy.
At this point we had a fairly complete list of roadmap items, however the
document itself was still more a brainstorm dump than a roadmap document.
So during the NumPy sprint after the conference on 14 July 2018 Ralf
Gommers and Stephan Hoyer sat together to reshape and clean up these
ideas. That resulted in [roadmap draft v2](
https://github.com/numpy/numpy/wiki/NumPy-roadmap-v2) - not yet fully
polished, but getting closer to something ready for acceptance.
## Act 3: NumPy sprint at BIDS
In-person meetings seem to be critical to moving a complex and important
document like a project roadmap forward. So the next draft was produced
during a second NumPy sprint at Berkeley during 23-27 July 2018. Present
were Stéfan van der Walt, Matti Picus, Tyler Reddy and Ralf Gommers. We
polished the document, worked out the [Scope of NumPy](
https://www.numpy.org/neps/scope.html) section in more detail, and
integrated the roadmap in the html docs. During the sprint a [pull
request](https://github.com/numpy/numpy/pull/11611) was sent, and [posted](
https://mail.python.org/pipermail/numpy-discussion/2018-July/thread.html#78…)
again to the mailing list. Everyone seemed happy, and after addressing the
final few review comments the roadmap was merged on 2 August 2018.
*Final thought: NumPy development is accelerating at the moment, and
in-person meetings and having the people and funding at BIDS to organize
sprints has been a huge help. See https://github.com/BIDS-numpy/docs for an
overview of what has been organized in the last year.*
-----BEGIN PGP SIGNED MESSAGE-----
Hash: SHA256
Hi all,
On behalf of the SciPy development team I'm pleased to announce
the release candidate SciPy 1.2.0rc2. Please help us test out this
release candidate -- the 1.2.x series will be an LTS release and the
last to support Python 2.7.
Sources and binary wheels can be found at:
https://pypi.org/project/scipy/
and at:
https://github.com/scipy/scipy/releases/tag/v1.2.0rc2
One of a few ways to install the release candidate with pip:
pip install scipy==1.2.0rc2
==========================
SciPy 1.2.0 Release Notes
==========================
Note: Scipy 1.2.0 is not released yet!
SciPy 1.2.0 is the culmination of 6 months of hard work. It contains
many new features, numerous bug-fixes, improved test coverage and better
documentation. There have been a number of deprecations and 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
1.2.x branch, and on adding new features on the master branch.
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
unified interface, ``root_scalar``
- New ``dual_annealing`` optimization method that combines stochastic and
local deterministic searching
- A new optimization algorithm, ``shgo`` (simplicial homology
global optimization) for derivative free optimization problems
- A new category of quaternion-based transformations are available in
`scipy.spatial.transform`
New features
============
`scipy.ndimage` improvements
---------------------------------
Proper spline coefficient calculations have been added for the ``mirror``,
``wrap``, and ``reflect`` modes of `scipy.ndimage.rotate`
`scipy.fftpack` improvements
---------------------------------
DCT-IV, DST-IV, DCT-I, and DST-I orthonormalization are now supported in
`scipy.fftpack`.
`scipy.interpolate` improvements
---------------------------------
`scipy.interpolate.pade` now accepts a new argument for the order of the
numerator
`scipy.cluster` improvements
-----------------------------
`scipy.cluster.vq.kmeans2` gained a new initialization method, kmeans++.
`scipy.special` improvements
-----------------------------
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
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
bracket and the number of derivatives available.
The so-called Algorithm 748 of Alefeld, Potra and Shi for root-finding
within
an enclosing interval has been added as `scipy.optimize.toms748`. This
provides
guaranteed convergence to a root with convergence rate per function
evaluation
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
convergence. The ``workers`` keyword accepts an ``int`` or map-like
callable,
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
(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
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
iteration. This makes it useful for exploring the solutions in a domain.
`scipy.optimize.newton` can now accept a scalar or an array
``MINPACK`` usage is now thread-safe, such that ``MINPACK`` + callbacks may
be used on multiple threads.
`scipy.signal` improvements
----------------------------
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
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
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
flat top of a peak.
``welch()`` and ``csd()`` methods in `scipy.signal` now support calculation
of a median average PSD, using ``average='mean'`` keyword
`scipy.sparse` improvements
----------------------------
The `scipy.sparse.bsr_matrix.tocsr` method is now implemented directly
instead
of converting via COO format, and the `scipy.sparse.bsr_matrix.tocsc` method
is now also routed via CSR conversion instead of COO. The efficiency of both
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.
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
smallest eigenvalues, and descending for the largest eigenvalues.
The `scipy.sparse.random` function is now faster and also supports integer
and
complex values by passing the appropriate value to the ``dtype`` argument.
`scipy.spatial` improvements
-----------------------------
The function `scipy.spatial.distance.jaccard` was modified to return 0
instead
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
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
rotations (`spatial.transform.Rotation.random`).
`scipy.stats` improvements
---------------------------
The Yeo-Johnson power transformation is now supported (``yeojohnson``,
``yeojohnson_llf``, ``yeojohnson_normmax``, ``yeojohnson_normplot``). Unlike
the Box-Cox transformation, the Yeo-Johnson transformation can accept
negative
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
discrete probability distribution.
``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,
and should have a modest improvement in performance
Levy Stable Parameter Estimation, PDF, and CDF calculations are now
supported
for `scipy.stats.levy_stable`.
The Brunner-Munzel test is now available as ``brunnermunzel`` in ``stats``
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,
or Hermitian matrices; the upper trapezoidal fat matrix RZ decomposition
routines are now available as well.
Deprecated features
===================
The functions ``hyp2f0``, ``hyp1f2`` and ``hyp3f0`` in ``scipy.special``
have
been deprecated.
Backwards incompatible changes
==============================
LAPACK version 3.4.0 or later is now required. Building with
Apple Accelerate is no longer supported.
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
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
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
information passed to the callback function.
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
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
some matrix shapes and a given seed.
`scipy.optimize.newton` will no longer use Halley's method in cases where it
negatively impacts convergence
Other changes
=============
Authors
=======
* @endolith
* @luzpaz
* Hameer Abbasi +
* akahard2dj +
* Anton Akhmerov
* Joseph Albert
* alexthomas93 +
* ashish +
* atpage +
* Blair Azzopardi +
* Yoshiki Vázquez Baeza
* Bence Bagi +
* Christoph Baumgarten
* Lucas Bellomo +
* BH4 +
* Aditya Bharti
* Max Bolingbroke
* François Boulogne
* Ward Bradt +
* Matthew Brett
* Evgeni Burovski
* Rafał Byczek +
* Alfredo Canziani +
* CJ Carey
* Lucía Cheung +
* Poom Chiarawongse +
* Jeanne Choo +
* Robert Cimrman
* Graham Clenaghan +
* cynthia-rempel +
* Johannes Damp +
* Jaime Fernandez del Rio
* Dowon +
* emmi474 +
* Stefan Endres +
* Thomas Etherington +
* Piotr Figiel
* Alex Fikl +
* fo40225 +
* Joseph Fox-Rabinovitz
* Lars G
* Abhinav Gautam +
* Stiaan Gerber +
* C.A.M. Gerlach +
* Ralf Gommers
* Todd Goodall
* Lars Grueter +
* Sylvain Gubian +
* Matt Haberland
* David Hagen
* Will Handley +
* Charles Harris
* Ian Henriksen
* Thomas Hisch +
* Theodore Hu
* Michael Hudson-Doyle +
* Nicolas Hug +
* jakirkham +
* Jakob Jakobson +
* James +
* Jan Schlüter
* jeanpauphilet +
* josephmernst +
* Kai +
* Kai-Striega +
* kalash04 +
* Toshiki Kataoka +
* Konrad0 +
* Tom Krauss +
* Johannes Kulick
* Lars Grüter +
* Eric Larson
* Denis Laxalde
* Will Lee +
* Katrin Leinweber +
* Yin Li +
* P. L. Lim +
* Jesse Livezey +
* Duncan Macleod +
* MatthewFlamm +
* Nikolay Mayorov
* Mike McClurg +
* Christian Meyer +
* Mark Mikofski
* Naoto Mizuno +
* mohmmadd +
* Nathan Musoke
* Anju Geetha Nair +
* Andrew Nelson
* Ayappan P +
* Nick Papior
* Haesun Park +
* Ronny Pfannschmidt +
* pijyoi +
* Ilhan Polat
* Anthony Polloreno +
* Ted Pudlik
* puenka
* Eric Quintero
* Pradeep Reddy Raamana +
* Vyas Ramasubramani +
* Ramon Viñas +
* Tyler Reddy
* Joscha Reimer
* Antonio H Ribeiro
* richardjgowers +
* Rob +
* robbystk +
* Lucas Roberts +
* rohan +
* Joaquin Derrac Rus +
* Josua Sassen +
* Bruce Sharpe +
* Max Shinn +
* Scott Sievert
* Sourav Singh
* Strahinja Lukić +
* Kai Striega +
* Shinya SUZUKI +
* Mike Toews +
* Piotr Uchwat
* Miguel de Val-Borro +
* Nicky van Foreest
* Paul van Mulbregt
* Gael Varoquaux
* Pauli Virtanen
* Stefan van der Walt
* Warren Weckesser
* Joshua Wharton +
* Bernhard M. Wiedemann +
* Eric Wieser
* Josh Wilson
* Tony Xiang +
* Roman Yurchak +
* Roy Zywina +
A total of 137 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
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
* `#3678 <https://github.com/scipy/scipy/issues/3678>`__:
_clough_tocher_2d_single direction between centroids
* `#4174 <https://github.com/scipy/scipy/issues/4174>`__: lobpcg "largest"
option invalid?
* `#5493 <https://github.com/scipy/scipy/issues/5493>`__: anderson_ksamp
p-values>1
* `#5743 <https://github.com/scipy/scipy/issues/5743>`__: slsqp fails to
detect infeasible problem
* `#6139 <https://github.com/scipy/scipy/issues/6139>`__:
scipy.optimize.linprog failed to find a feasible starting point...
* `#6358 <https://github.com/scipy/scipy/issues/6358>`__: stats: docstring
for `vonmises_line` points to `vonmises_line`...
* `#6498 <https://github.com/scipy/scipy/issues/6498>`__: runtests.py is
missing in pypi distfile
* `#7426 <https://github.com/scipy/scipy/issues/7426>`__:
scipy.stats.ksone(n).pdf(x) returns nan for positive values of...
* `#7455 <https://github.com/scipy/scipy/issues/7455>`__:
scipy.stats.ksone.pdf(2,x) return incorrect values for x near...
* `#7456 <https://github.com/scipy/scipy/issues/7456>`__:
scipy.special.smirnov and scipy.special.smirnovi have accuracy...
* `#7492 <https://github.com/scipy/scipy/issues/7492>`__:
scipy.special.kolmogorov(x)/kolmogi(p) inefficient, inaccurate...
* `#7914 <https://github.com/scipy/scipy/issues/7914>`__: TravisCI not
failing when it should for -OO run
* `#8064 <https://github.com/scipy/scipy/issues/8064>`__: linalg.solve test
crashes on Windows
* `#8212 <https://github.com/scipy/scipy/issues/8212>`__: LAPACK
Rectangular Full Packed routines
* `#8256 <https://github.com/scipy/scipy/issues/8256>`__:
differential_evolution bug converges to wrong results in complex...
* `#8443 <https://github.com/scipy/scipy/issues/8443>`__: Deprecate
`hyp2f0`, `hyp1f2`, and `hyp3f0`?
* `#8452 <https://github.com/scipy/scipy/issues/8452>`__: DOC: ARPACK
tutorial has two conflicting equations
* `#8680 <https://github.com/scipy/scipy/issues/8680>`__: scipy fails
compilation when building from source
* `#8686 <https://github.com/scipy/scipy/issues/8686>`__: Division by zero
in _trustregion.py when x0 is exactly equal...
* `#8700 <https://github.com/scipy/scipy/issues/8700>`__: _MINPACK_LOCK not
held when calling into minpack from least_squares
* `#8786 <https://github.com/scipy/scipy/issues/8786>`__: erroneous moment
values for t-distribution
* `#8791 <https://github.com/scipy/scipy/issues/8791>`__: Checking COLA
condition in istft should be optional (or omitted)
* `#8843 <https://github.com/scipy/scipy/issues/8843>`__: imresize cannot
be deprecated just yet
* `#8844 <https://github.com/scipy/scipy/issues/8844>`__: Inverse Wishart
Log PDF Incorrect for Non-diagonal Scale Matrix?
* `#8878 <https://github.com/scipy/scipy/issues/8878>`__: vonmises and
vonmises_line in stats: vonmises wrong and superfluous?
* `#8895 <https://github.com/scipy/scipy/issues/8895>`__: v1.1.0
`ndi.rotate` documentation – reused parameters not filled...
* `#8900 <https://github.com/scipy/scipy/issues/8900>`__: Missing complex
conjugation in scipy.sparse.linalg.LinearOperator
* `#8904 <https://github.com/scipy/scipy/issues/8904>`__: BUG: if zero
derivative at root, then Newton fails with RuntimeWarning
* `#8911 <https://github.com/scipy/scipy/issues/8911>`__:
make_interp_spline bc_type incorrect input interpretation
* `#8942 <https://github.com/scipy/scipy/issues/8942>`__: MAINT: Refactor
`_linprog.py` and `_linprog_ip.py` to remove...
* `#8947 <https://github.com/scipy/scipy/issues/8947>`__: np.int64 in
scipy.fftpack.next_fast_len
* `#9020 <https://github.com/scipy/scipy/issues/9020>`__: BUG:
linalg.subspace_angles gives wrong results
* `#9033 <https://github.com/scipy/scipy/issues/9033>`__:
scipy.stats.normaltest sometimes gives incorrect returns b/c...
* `#9036 <https://github.com/scipy/scipy/issues/9036>`__: Bizarre times for
`scipy.sparse.rand` function with 'low' density...
* `#9044 <https://github.com/scipy/scipy/issues/9044>`__:
optimize.minimize(method=`trust-constr`) result dict does not...
* `#9071 <https://github.com/scipy/scipy/issues/9071>`__: doc/linalg: add
cho_solve_banded to see also of cholesky_banded
* `#9082 <https://github.com/scipy/scipy/issues/9082>`__: eigenvalue
sorting in scipy.sparse.linalg.eigsh
* `#9086 <https://github.com/scipy/scipy/issues/9086>`__:
signaltools.py:491: FutureWarning: Using a non-tuple sequence...
* `#9091 <https://github.com/scipy/scipy/issues/9091>`__:
test_spline_filter failure on 32-bit
* `#9122 <https://github.com/scipy/scipy/issues/9122>`__: Typo on scipy
minimization tutorial
* `#9135 <https://github.com/scipy/scipy/issues/9135>`__: doc error at
https://docs.scipy.org/doc/scipy/reference/tutorial/stats/discrete_poisson.…
* `#9167 <https://github.com/scipy/scipy/issues/9167>`__: DOC: BUG: typo in
ndimage LowLevelCallable tutorial example
* `#9169 <https://github.com/scipy/scipy/issues/9169>`__: truncnorm does
not work if b < a in scipy.stats
* `#9250 <https://github.com/scipy/scipy/issues/9250>`__:
scipy.special.tests.test_mpmath::TestSystematic::test_pcfw fails...
* `#9259 <https://github.com/scipy/scipy/issues/9259>`__: rv.expect() ==
rv.mean() is false for rv.mean() == nan (and inf)
* `#9286 <https://github.com/scipy/scipy/issues/9286>`__: DOC: Rosenbrock
expression in optimize.minimize tutorial
* `#9316 <https://github.com/scipy/scipy/issues/9316>`__: SLSQP fails in
nested optimization
* `#9337 <https://github.com/scipy/scipy/issues/9337>`__:
scipy.signal.find_peaks key typo in documentation
* `#9345 <https://github.com/scipy/scipy/issues/9345>`__: Example from
documentation of scipy.sparse.linalg.eigs raises...
* `#9383 <https://github.com/scipy/scipy/issues/9383>`__: Default value for
"mode" in "ndimage.shift"
* `#9419 <https://github.com/scipy/scipy/issues/9419>`__: dual_annealing
off by one in the number of iterations
* `#9442 <https://github.com/scipy/scipy/issues/9442>`__: Error in
Defintion of Rosenbrock Function
* `#9453 <https://github.com/scipy/scipy/issues/9453>`__: TST:
test_eigs_consistency() doesn't have consistent results
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
* `#8098 <https://github.com/scipy/scipy/pull/8098>`__: ENH: Add shgo for
global optimization of NLPs.
* `#8203 <https://github.com/scipy/scipy/pull/8203>`__: ENH: adding
simulated dual annealing to optimize
* `#8259 <https://github.com/scipy/scipy/pull/8259>`__: Option to follow
original Storn and Price algorithm and its parallelisation
* `#8293 <https://github.com/scipy/scipy/pull/8293>`__: ENH add
ratio-of-uniforms method for rv generation to scipy.stats
* `#8294 <https://github.com/scipy/scipy/pull/8294>`__: BUG: Fix slowness
in stats.mode
* `#8295 <https://github.com/scipy/scipy/pull/8295>`__: ENH: add Jensen
Shannon distance to `scipy.spatial.distance`
* `#8357 <https://github.com/scipy/scipy/pull/8357>`__: ENH: vectorize
scalar zero-search-functions
* `#8397 <https://github.com/scipy/scipy/pull/8397>`__: Add `fs=` parameter
to filter design functions
* `#8537 <https://github.com/scipy/scipy/pull/8537>`__: ENH: Implement mode
parameter for spline filtering.
* `#8558 <https://github.com/scipy/scipy/pull/8558>`__: ENH: small speedup
for stats.gaussian_kde
* `#8560 <https://github.com/scipy/scipy/pull/8560>`__: BUG: fix p-value
calc of anderson_ksamp in scipy.stats
* `#8614 <https://github.com/scipy/scipy/pull/8614>`__: ENH: correct
p-values for stats.kendalltau and stats.mstats.kendalltau
* `#8670 <https://github.com/scipy/scipy/pull/8670>`__: ENH: Require Lapack
3.4.0
* `#8683 <https://github.com/scipy/scipy/pull/8683>`__: Correcting kmeans
documentation
* `#8725 <https://github.com/scipy/scipy/pull/8725>`__: MAINT: Cleanup
scipy.optimize.leastsq
* `#8726 <https://github.com/scipy/scipy/pull/8726>`__: BUG: Fix
_get_output in scipy.ndimage to support string
* `#8733 <https://github.com/scipy/scipy/pull/8733>`__: MAINT: stats: A bit
of clean up.
* `#8737 <https://github.com/scipy/scipy/pull/8737>`__: BUG: Improve
numerical precision/convergence failures of smirnov/kolmogorov
* `#8738 <https://github.com/scipy/scipy/pull/8738>`__: MAINT: stats: A bit
of clean up in test_distributions.py.
* `#8740 <https://github.com/scipy/scipy/pull/8740>`__: BF/ENH: make
minpack thread safe
* `#8742 <https://github.com/scipy/scipy/pull/8742>`__: BUG: Fix division
by zero in trust-region optimization methods
* `#8746 <https://github.com/scipy/scipy/pull/8746>`__: MAINT: signal: Fix
a docstring of a private function, and fix...
* `#8750 <https://github.com/scipy/scipy/pull/8750>`__: DOC clarified
description of norminvgauss in scipy.stats
* `#8753 <https://github.com/scipy/scipy/pull/8753>`__: DOC: signal: Fix a
plot title in the chirp docstring.
* `#8755 <https://github.com/scipy/scipy/pull/8755>`__: DOC: MAINT: Fix
link to the wheel documentation in developer...
* `#8760 <https://github.com/scipy/scipy/pull/8760>`__: BUG: stats:
boltzmann wasn't setting the upper bound.
* `#8763 <https://github.com/scipy/scipy/pull/8763>`__: [DOC] Improved
scipy.cluster.hierarchy documentation
* `#8765 <https://github.com/scipy/scipy/pull/8765>`__: DOC: added example
for scipy.stat.mstats.tmin
* `#8788 <https://github.com/scipy/scipy/pull/8788>`__: DOC: fix definition
of optional `disp` parameter
* `#8802 <https://github.com/scipy/scipy/pull/8802>`__: MAINT: Suppress
dd_real unused function compiler warnings.
* `#8803 <https://github.com/scipy/scipy/pull/8803>`__: ENH: Add
full_output support to optimize.newton()
* `#8804 <https://github.com/scipy/scipy/pull/8804>`__: MAINT: stats cleanup
* `#8808 <https://github.com/scipy/scipy/pull/8808>`__: DOC: add note about
isinstance for frozen rvs
* `#8812 <https://github.com/scipy/scipy/pull/8812>`__: Updated numpydoc
submodule
* `#8813 <https://github.com/scipy/scipy/pull/8813>`__: MAINT: stats: Fix
multinomial docstrings, and do some clean up.
* `#8816 <https://github.com/scipy/scipy/pull/8816>`__: BUG: fixed _stats
of t-distribution in scipy.stats
* `#8817 <https://github.com/scipy/scipy/pull/8817>`__: BUG: ndimage: Fix
validation of the origin argument in correlate...
* `#8822 <https://github.com/scipy/scipy/pull/8822>`__: BUG: integrate: Fix
crash with repeated t values in odeint.
* `#8832 <https://github.com/scipy/scipy/pull/8832>`__: Hyperlink DOIs
against preferred resolver
* `#8837 <https://github.com/scipy/scipy/pull/8837>`__: BUG: sparse: Ensure
correct dtype for sparse comparison operations.
* `#8839 <https://github.com/scipy/scipy/pull/8839>`__: DOC: stats: A few
tweaks to the linregress docstring.
* `#8846 <https://github.com/scipy/scipy/pull/8846>`__: BUG: stats: Fix
logpdf method of invwishart.
* `#8849 <https://github.com/scipy/scipy/pull/8849>`__: DOC: signal: Fixed
mistake in the firwin docstring.
* `#8854 <https://github.com/scipy/scipy/pull/8854>`__: DOC: fix type
descriptors in ltisys documentation
* `#8865 <https://github.com/scipy/scipy/pull/8865>`__: Fix tiny typo in
docs for chi2 pdf
* `#8870 <https://github.com/scipy/scipy/pull/8870>`__: Fixes related to
invertibility of STFT
* `#8872 <https://github.com/scipy/scipy/pull/8872>`__: ENH: special: Add
the softmax function
* `#8874 <https://github.com/scipy/scipy/pull/8874>`__: DOC correct gamma
function in docstrings in scipy.stats
* `#8876 <https://github.com/scipy/scipy/pull/8876>`__: ENH: Added TOMS
Algorithm 748 as 1-d root finder; 17 test function...
* `#8882 <https://github.com/scipy/scipy/pull/8882>`__: ENH: Only use
Halley's adjustment to Newton if close enough.
* `#8883 <https://github.com/scipy/scipy/pull/8883>`__: FIX: optimize: make
jac and hess truly optional for 'trust-constr'
* `#8885 <https://github.com/scipy/scipy/pull/8885>`__: TST: Do not error
on warnings raised about non-tuple indexing.
* `#8887 <https://github.com/scipy/scipy/pull/8887>`__: MAINT: filter out
np.matrix PendingDeprecationWarning's in numpy...
* `#8889 <https://github.com/scipy/scipy/pull/8889>`__: DOC: optimize:
separate legacy interfaces from new ones
* `#8890 <https://github.com/scipy/scipy/pull/8890>`__: ENH: Add
optimize.root_scalar() as a universal dispatcher for...
* `#8899 <https://github.com/scipy/scipy/pull/8899>`__: DCT-IV, DST-IV and
DCT-I, DST-I orthonormalization support in...
* `#8901 <https://github.com/scipy/scipy/pull/8901>`__: MAINT: Reorganize
flapack.pyf.src file
* `#8907 <https://github.com/scipy/scipy/pull/8907>`__: BUG: ENH: Check if
guess for newton is already zero before checking...
* `#8908 <https://github.com/scipy/scipy/pull/8908>`__: ENH: Make sorting
optional for cKDTree.query_ball_point()
* `#8910 <https://github.com/scipy/scipy/pull/8910>`__: DOC: sparse.csgraph
simple examples.
* `#8914 <https://github.com/scipy/scipy/pull/8914>`__: DOC: interpolate:
fix equivalences of string aliases
* `#8918 <https://github.com/scipy/scipy/pull/8918>`__: add
float_control(precise, on) to _fpumode.c
* `#8919 <https://github.com/scipy/scipy/pull/8919>`__: MAINT: interpolate:
improve error messages for common `bc_type`...
* `#8920 <https://github.com/scipy/scipy/pull/8920>`__: DOC: update
Contributing to SciPy to say "prefer no PEP8 only...
* `#8924 <https://github.com/scipy/scipy/pull/8924>`__: MAINT: special:
deprecate `hyp2f0`, `hyp1f2`, and `hyp3f0`
* `#8927 <https://github.com/scipy/scipy/pull/8927>`__: MAINT: special:
remove `errprint`
* `#8932 <https://github.com/scipy/scipy/pull/8932>`__: Fix broadcasting
scale arg of entropy
* `#8936 <https://github.com/scipy/scipy/pull/8936>`__: Fix (some)
non-tuple index warnings
* `#8937 <https://github.com/scipy/scipy/pull/8937>`__: ENH: implement
sparse matrix BSR to CSR conversion directly.
* `#8938 <https://github.com/scipy/scipy/pull/8938>`__: DOC: add
@_ni_docstrings.docfiller in ndimage.rotate
* `#8940 <https://github.com/scipy/scipy/pull/8940>`__: Update
_discrete_distns.py
* `#8943 <https://github.com/scipy/scipy/pull/8943>`__: DOC: Finish
dangling sentence in `convolve` docstring
* `#8944 <https://github.com/scipy/scipy/pull/8944>`__: MAINT: Address
tuple indexing and warnings
* `#8945 <https://github.com/scipy/scipy/pull/8945>`__: ENH:
spatial.transform.Rotation [GSOC2018]
* `#8950 <https://github.com/scipy/scipy/pull/8950>`__: csgraph Dijkstra
function description rewording
* `#8953 <https://github.com/scipy/scipy/pull/8953>`__: DOC, MAINT: HTTP ->
HTTPS, and other linkrot fixes
* `#8955 <https://github.com/scipy/scipy/pull/8955>`__: BUG: np.int64 in
scipy.fftpack.next_fast_len
* `#8958 <https://github.com/scipy/scipy/pull/8958>`__: MAINT: Add more
descriptive error message for phase one simplex.
* `#8962 <https://github.com/scipy/scipy/pull/8962>`__: BUG: sparse.linalg:
add missing conjugate to _ScaledLinearOperator.adjoint
* `#8963 <https://github.com/scipy/scipy/pull/8963>`__: BUG: sparse.linalg:
downgrade LinearOperator TypeError to warning
* `#8965 <https://github.com/scipy/scipy/pull/8965>`__: ENH: Wrapped RFP
format and RZ decomposition routines
* `#8969 <https://github.com/scipy/scipy/pull/8969>`__: MAINT: doc and code
fixes for optimize.newton
* `#8970 <https://github.com/scipy/scipy/pull/8970>`__: Added 'average'
keyword for welch/csd to enable median averaging
* `#8971 <https://github.com/scipy/scipy/pull/8971>`__: Better imresize
deprecation warning
* `#8972 <https://github.com/scipy/scipy/pull/8972>`__: MAINT: Switch
np.where(c) for np.nonzero(c)
* `#8975 <https://github.com/scipy/scipy/pull/8975>`__: MAINT: Fix
warning-based failures
* `#8979 <https://github.com/scipy/scipy/pull/8979>`__: DOC: fix
description of count_sort keyword of dendrogram
* `#8982 <https://github.com/scipy/scipy/pull/8982>`__: MAINT: optimize:
Fixed minor mistakes in test_linprog.py (#8978)
* `#8984 <https://github.com/scipy/scipy/pull/8984>`__: BUG: sparse.linalg:
ensure expm casts integer inputs to float
* `#8986 <https://github.com/scipy/scipy/pull/8986>`__: BUG:
optimize/slsqp: do not exit with convergence on steps where...
* `#8989 <https://github.com/scipy/scipy/pull/8989>`__: MAINT: use
collections.abc in basinhopping
* `#8990 <https://github.com/scipy/scipy/pull/8990>`__: ENH extend p-values
of anderson_ksamp in scipy.stats
* `#8991 <https://github.com/scipy/scipy/pull/8991>`__: ENH: Weighted kde
* `#8993 <https://github.com/scipy/scipy/pull/8993>`__: ENH:
spatial.transform.Rotation.random [GSOC 2018]
* `#8994 <https://github.com/scipy/scipy/pull/8994>`__: ENH:
spatial.transform.Slerp [GSOC 2018]
* `#8995 <https://github.com/scipy/scipy/pull/8995>`__: TST: time.time in
test
* `#9007 <https://github.com/scipy/scipy/pull/9007>`__: Fix typo in
fftpack.rst
* `#9013 <https://github.com/scipy/scipy/pull/9013>`__: Added correct
plotting code for two sided output from spectrogram
* `#9014 <https://github.com/scipy/scipy/pull/9014>`__: BUG:
differential_evolution with inf objective functions
* `#9017 <https://github.com/scipy/scipy/pull/9017>`__: BUG: fixed #8446
corner case for asformat(array|dense)
* `#9018 <https://github.com/scipy/scipy/pull/9018>`__: MAINT:
_lib/ccallback: remove unused code
* `#9021 <https://github.com/scipy/scipy/pull/9021>`__: BUG: Issue with
subspace_angles
* `#9022 <https://github.com/scipy/scipy/pull/9022>`__: DOC: Added "See
Also" section to lombscargle docstring
* `#9034 <https://github.com/scipy/scipy/pull/9034>`__: BUG: Fix tolerance
printing behavior, remove meaningless tol...
* `#9035 <https://github.com/scipy/scipy/pull/9035>`__: TST: improve
signal.bsplines test coverage
* `#9037 <https://github.com/scipy/scipy/pull/9037>`__: ENH: add a new init
method for k-means
* `#9039 <https://github.com/scipy/scipy/pull/9039>`__: DOC: Add examples
to fftpack.irfft docstrings
* `#9048 <https://github.com/scipy/scipy/pull/9048>`__: ENH:
scipy.sparse.random
* `#9050 <https://github.com/scipy/scipy/pull/9050>`__: BUG:
scipy.io.hb_write: fails for matrices not in csc format
* `#9051 <https://github.com/scipy/scipy/pull/9051>`__: MAINT: Fix slow
sparse.rand for k < mn/3 (#9036).
* `#9054 <https://github.com/scipy/scipy/pull/9054>`__: MAINT: spatial:
Explicitly initialize LAPACK output parameters.
* `#9055 <https://github.com/scipy/scipy/pull/9055>`__: DOC: Add examples
to scipy.special docstrings
* `#9056 <https://github.com/scipy/scipy/pull/9056>`__: ENH: Use one thread
in OpenBLAS
* `#9059 <https://github.com/scipy/scipy/pull/9059>`__: DOC: Update README
with link to Code of Conduct
* `#9060 <https://github.com/scipy/scipy/pull/9060>`__: BLD: remove support
for the Bento build system.
* `#9062 <https://github.com/scipy/scipy/pull/9062>`__: DOC add sections to
overview in scipy.stats
* `#9066 <https://github.com/scipy/scipy/pull/9066>`__: BUG: Correct
"remez" error message
* `#9069 <https://github.com/scipy/scipy/pull/9069>`__: DOC: update linalg
section of roadmap for LAPACK versions.
* `#9079 <https://github.com/scipy/scipy/pull/9079>`__: MAINT: add
spatial.transform to refguide check; complete some...
* `#9081 <https://github.com/scipy/scipy/pull/9081>`__: MAINT: Add warnings
if pivot value is close to tolerance in linprog(method='simplex')
* `#9084 <https://github.com/scipy/scipy/pull/9084>`__: BUG fix incorrect
p-values of kurtosistest in scipy.stats
* `#9095 <https://github.com/scipy/scipy/pull/9095>`__: DOC: add sections
to mstats overview in scipy.stats
* `#9096 <https://github.com/scipy/scipy/pull/9096>`__: BUG: Add test for
Stackoverflow example from issue 8174.
* `#9101 <https://github.com/scipy/scipy/pull/9101>`__: ENH: add Siegel
slopes (robust regression) to scipy.stats
* `#9105 <https://github.com/scipy/scipy/pull/9105>`__: allow
resample_poly() to output float32 for float32 inputs.
* `#9112 <https://github.com/scipy/scipy/pull/9112>`__: MAINT: optimize:
make trust-constr accept constraint dict (#9043)
* `#9118 <https://github.com/scipy/scipy/pull/9118>`__: Add doc entry to
cholesky_banded
* `#9120 <https://github.com/scipy/scipy/pull/9120>`__: eigsh documentation
parameters
* `#9125 <https://github.com/scipy/scipy/pull/9125>`__: interpolative:
correctly reconstruct full rank matrices
* `#9126 <https://github.com/scipy/scipy/pull/9126>`__: MAINT: Use warnings
for unexpected peak properties
* `#9129 <https://github.com/scipy/scipy/pull/9129>`__: BUG: Do not catch
and silence KeyboardInterrupt
* `#9131 <https://github.com/scipy/scipy/pull/9131>`__: DOC: Correct the
typo in scipy.optimize tutorial page
* `#9133 <https://github.com/scipy/scipy/pull/9133>`__: FIX: Avoid use of
bare except
* `#9134 <https://github.com/scipy/scipy/pull/9134>`__: DOC: Update of
'return_eigenvectors' description
* `#9137 <https://github.com/scipy/scipy/pull/9137>`__: DOC: typo fixes for
discrete Poisson tutorial
* `#9139 <https://github.com/scipy/scipy/pull/9139>`__: FIX: Doctest
failure in optimize tutorial
* `#9143 <https://github.com/scipy/scipy/pull/9143>`__: DOC: missing sigma
in Pearson r formula
* `#9145 <https://github.com/scipy/scipy/pull/9145>`__: MAINT: Refactor
linear programming solvers
* `#9149 <https://github.com/scipy/scipy/pull/9149>`__: FIX: Make
scipy.odr.ODR ifixx equal to its data.fix if given
* `#9156 <https://github.com/scipy/scipy/pull/9156>`__: DOC: special:
Mention the sigmoid function in the expit docstring.
* `#9160 <https://github.com/scipy/scipy/pull/9160>`__: Fixed a latex
delimiter error in levy()
* `#9170 <https://github.com/scipy/scipy/pull/9170>`__: DOC: correction /
update of docstrings of distributions in scipy.stats
* `#9171 <https://github.com/scipy/scipy/pull/9171>`__: better description
of the hierarchical clustering parameter
* `#9174 <https://github.com/scipy/scipy/pull/9174>`__: domain check for a
< b in stats.truncnorm
* `#9175 <https://github.com/scipy/scipy/pull/9175>`__: DOC: Minor grammar
fix
* `#9176 <https://github.com/scipy/scipy/pull/9176>`__: BUG:
CloughTocher2DInterpolator: fix miscalculation at neighborless...
* `#9177 <https://github.com/scipy/scipy/pull/9177>`__: BUILD: Document the
"clean" target in the doc/Makefile.
* `#9178 <https://github.com/scipy/scipy/pull/9178>`__: MAINT: make
refguide-check more robust for printed numpy arrays
* `#9186 <https://github.com/scipy/scipy/pull/9186>`__: MAINT: Remove
np.ediff1d occurence
* `#9188 <https://github.com/scipy/scipy/pull/9188>`__: DOC: correct typo
in extending ndimage with C
* `#9190 <https://github.com/scipy/scipy/pull/9190>`__: ENH: Support
specifying axes for fftconvolve
* `#9192 <https://github.com/scipy/scipy/pull/9192>`__: MAINT: optimize:
fixed @pv style suggestions from #9112
* `#9200 <https://github.com/scipy/scipy/pull/9200>`__: Fix
make_interp_spline(..., k=0 or 1, axis<0)
* `#9201 <https://github.com/scipy/scipy/pull/9201>`__: BUG:
sparse.linalg/gmres: use machine eps in breakdown check
* `#9204 <https://github.com/scipy/scipy/pull/9204>`__: MAINT: fix up
stats.spearmanr and match mstats.spearmanr with...
* `#9206 <https://github.com/scipy/scipy/pull/9206>`__: MAINT: include
benchmarks and dev files in sdist.
* `#9208 <https://github.com/scipy/scipy/pull/9208>`__: TST: signal: bump
bsplines test tolerance for complex data
* `#9210 <https://github.com/scipy/scipy/pull/9210>`__: TST: mark tests as
slow, fix missing random seed
* `#9211 <https://github.com/scipy/scipy/pull/9211>`__: ENH: add capability
to specify orders in pade func
* `#9217 <https://github.com/scipy/scipy/pull/9217>`__: MAINT: Include
``success`` and ``nit`` in OptimizeResult returned...
* `#9222 <https://github.com/scipy/scipy/pull/9222>`__: ENH: interpolate:
Use scipy.spatial.distance to speed-up Rbf
* `#9229 <https://github.com/scipy/scipy/pull/9229>`__: MNT: Fix Fourier
filter double case
* `#9233 <https://github.com/scipy/scipy/pull/9233>`__: BUG:
spatial/distance: fix pdist/cdist performance regression...
* `#9234 <https://github.com/scipy/scipy/pull/9234>`__: FIX: Proper
suppression
* `#9235 <https://github.com/scipy/scipy/pull/9235>`__: BENCH: rationalize
slow benchmarks + miscellaneous fixes
* `#9238 <https://github.com/scipy/scipy/pull/9238>`__: BENCH: limit number
of parameter combinations in spatial.*KDTree...
* `#9239 <https://github.com/scipy/scipy/pull/9239>`__: DOC: stats: Fix
LaTeX markup of a couple distribution PDFs.
* `#9241 <https://github.com/scipy/scipy/pull/9241>`__: ENH: Evaluate
plateau size during peak finding
* `#9242 <https://github.com/scipy/scipy/pull/9242>`__: ENH: stats:
Implement _ppf and _logpdf for crystalball, and do...
* `#9246 <https://github.com/scipy/scipy/pull/9246>`__: DOC: Properly
render versionadded directive in HTML documentation
* `#9255 <https://github.com/scipy/scipy/pull/9255>`__: DOC: mention
RootResults in optimization reference guide
* `#9260 <https://github.com/scipy/scipy/pull/9260>`__: TST: relax some
tolerances so tests pass with x87 math
* `#9264 <https://github.com/scipy/scipy/pull/9264>`__: TST Use
assert_raises "match" parameter instead of the "message"...
* `#9267 <https://github.com/scipy/scipy/pull/9267>`__: DOC: clarify
expect() return val when moment is inf/nan
* `#9272 <https://github.com/scipy/scipy/pull/9272>`__: DOC: Add
description of default bounds to linprog
* `#9277 <https://github.com/scipy/scipy/pull/9277>`__: MAINT:
sparse/linalg: make test deterministic
* `#9278 <https://github.com/scipy/scipy/pull/9278>`__: MAINT: interpolate:
pep8 cleanup in test_polyint
* `#9279 <https://github.com/scipy/scipy/pull/9279>`__: Fixed docstring for
resample
* `#9280 <https://github.com/scipy/scipy/pull/9280>`__: removed first check
for float in get_sum_dtype
* `#9281 <https://github.com/scipy/scipy/pull/9281>`__: BUG: only accept 1d
input for bartlett / levene in scipy.stats
* `#9282 <https://github.com/scipy/scipy/pull/9282>`__: MAINT: dense_output
and t_eval are mutually exclusive inputs
* `#9283 <https://github.com/scipy/scipy/pull/9283>`__: MAINT: add docs and
do some cleanups in interpolate.Rbf
* `#9288 <https://github.com/scipy/scipy/pull/9288>`__: Run
distance_transform_edt tests on all types
* `#9294 <https://github.com/scipy/scipy/pull/9294>`__: DOC: fix the
formula typo
* `#9298 <https://github.com/scipy/scipy/pull/9298>`__: MAINT:
optimize/trust-constr: restore .niter attribute for backward-compat
* `#9299 <https://github.com/scipy/scipy/pull/9299>`__: DOC: clarification
of default rvs method in scipy.stats
* `#9301 <https://github.com/scipy/scipy/pull/9301>`__: MAINT: removed
unused import sys
* `#9302 <https://github.com/scipy/scipy/pull/9302>`__: MAINT: removed
unused imports
* `#9303 <https://github.com/scipy/scipy/pull/9303>`__: DOC: signal: Refer
to fs instead of nyq in the firwin docstring.
* `#9305 <https://github.com/scipy/scipy/pull/9305>`__: ENH: Added
Yeo-Johnson power transformation
* `#9306 <https://github.com/scipy/scipy/pull/9306>`__: ENH - add dual
annealing
* `#9309 <https://github.com/scipy/scipy/pull/9309>`__: ENH add the
yulesimon distribution to scipy.stats
* `#9317 <https://github.com/scipy/scipy/pull/9317>`__: Nested SLSQP bug
fix.
* `#9320 <https://github.com/scipy/scipy/pull/9320>`__: MAINT: stats: avoid
underflow in stats.geom.ppf
* `#9326 <https://github.com/scipy/scipy/pull/9326>`__: Add example for
Rosenbrock function
* `#9332 <https://github.com/scipy/scipy/pull/9332>`__: Sort file lists
* `#9340 <https://github.com/scipy/scipy/pull/9340>`__: Fix typo in
find_peaks documentation
* `#9343 <https://github.com/scipy/scipy/pull/9343>`__: MAINT Use np.full
when possible
* `#9344 <https://github.com/scipy/scipy/pull/9344>`__: DOC: added examples
to docstring of dirichlet class
* `#9346 <https://github.com/scipy/scipy/pull/9346>`__: DOC: Fix import of
scipy.sparse.linalg in example (#9345)
* `#9350 <https://github.com/scipy/scipy/pull/9350>`__: Fix interpolate
read only
* `#9351 <https://github.com/scipy/scipy/pull/9351>`__: MAINT: special.erf:
use the x->-x symmetry
* `#9356 <https://github.com/scipy/scipy/pull/9356>`__: Fix documentation
typo
* `#9358 <https://github.com/scipy/scipy/pull/9358>`__: DOC: improve doc
for ksone and kstwobign in scipy.stats
* `#9362 <https://github.com/scipy/scipy/pull/9362>`__: DOC: Change
datatypes of A matrices in linprog
* `#9364 <https://github.com/scipy/scipy/pull/9364>`__: MAINT: Adds
implicit none to fftpack fortran sources
* `#9369 <https://github.com/scipy/scipy/pull/9369>`__: DOC: minor tweak to
CoC (updated NumFOCUS contact address).
* `#9373 <https://github.com/scipy/scipy/pull/9373>`__: Fix exception if
python is called with -OO option
* `#9374 <https://github.com/scipy/scipy/pull/9374>`__: FIX: AIX
compilation issue with NAN and INFINITY
* `#9376 <https://github.com/scipy/scipy/pull/9376>`__: COBLYA -> COBYLA in
docs
* `#9377 <https://github.com/scipy/scipy/pull/9377>`__: DOC: Add examples
integrate: fixed_quad and quadrature
* `#9379 <https://github.com/scipy/scipy/pull/9379>`__: MAINT: TST: Make
tests NumPy 1.8 compatible
* `#9385 <https://github.com/scipy/scipy/pull/9385>`__: CI: On Travis
matrix "OPTIMIZE=-OO" flag ignored
* `#9387 <https://github.com/scipy/scipy/pull/9387>`__: Fix defaut value
for 'mode' in 'ndimage.shift' in the doc
* `#9392 <https://github.com/scipy/scipy/pull/9392>`__: BUG: rank has to be
integer in rank_filter: fixed issue 9388
* `#9399 <https://github.com/scipy/scipy/pull/9399>`__: DOC: Misc. typos
* `#9400 <https://github.com/scipy/scipy/pull/9400>`__: TST: stats: Fix the
expected r-value of a linregress test.
* `#9405 <https://github.com/scipy/scipy/pull/9405>`__: BUG: np.hstack does
not accept generator expressions
* `#9408 <https://github.com/scipy/scipy/pull/9408>`__: ENH: linalg:
Shorter ill-conditioned warning message
* `#9418 <https://github.com/scipy/scipy/pull/9418>`__: DOC: Fix ndimage
docstrings and reduce doc build warnings
* `#9421 <https://github.com/scipy/scipy/pull/9421>`__: DOC: Add missing
docstring examples in scipy.spatial
* `#9422 <https://github.com/scipy/scipy/pull/9422>`__: DOC: Add an example
to integrate.newton_cotes
* `#9427 <https://github.com/scipy/scipy/pull/9427>`__: BUG: Fixed defect
with maxiter #9419 in dual annealing
* `#9431 <https://github.com/scipy/scipy/pull/9431>`__: BENCH: Add dual
annealing to scipy benchmark (see #9415)
* `#9435 <https://github.com/scipy/scipy/pull/9435>`__: DOC: Add docstring
examples for stats.binom_test
* `#9443 <https://github.com/scipy/scipy/pull/9443>`__: DOC: Fix the order
of indices in optimize tutorial
* `#9444 <https://github.com/scipy/scipy/pull/9444>`__: MAINT: interpolate:
use operator.index for checking/coercing...
* `#9445 <https://github.com/scipy/scipy/pull/9445>`__: DOC: Added missing
example to stats.mstats.kruskal
* `#9446 <https://github.com/scipy/scipy/pull/9446>`__: DOC: Add note about
version changed for jaccard distance
* `#9447 <https://github.com/scipy/scipy/pull/9447>`__: BLD: version-script
handling in setup.py
* `#9448 <https://github.com/scipy/scipy/pull/9448>`__: TST: skip a
problematic linalg test
* `#9449 <https://github.com/scipy/scipy/pull/9449>`__: TST: fix missing
seed in lobpcg test.
* `#9456 <https://github.com/scipy/scipy/pull/9456>`__: TST:
test_eigs_consistency() now sorts output
Checksums
=========
MD5
~~~
8d056b1b8aabbbcb34116af4f132688c
scipy-1.2.0rc2-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
53097b88fa71ca0d8bf1105f431682bf
scipy-1.2.0rc2-cp27-cp27m-manylinux1_i686.whl
180c35a669c61f5aa487dcb0cb18f8ab
scipy-1.2.0rc2-cp27-cp27m-manylinux1_x86_64.whl
0d4008cb8e25953b1e23a32c660684bd scipy-1.2.0rc2-cp27-cp27m-win32.whl
35d2091830466b6e37a672f59eae2f54 scipy-1.2.0rc2-cp27-cp27m-win_amd64.whl
0b6f7b8c0b8f1324ef2d87b7846fa6c5
scipy-1.2.0rc2-cp27-cp27mu-manylinux1_i686.whl
c91784a0889bb921b524555c68e224eb
scipy-1.2.0rc2-cp27-cp27mu-manylinux1_x86_64.whl
748d952957be7c48def5e77ccfbfa9fe
scipy-1.2.0rc2-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
ea7db734b9e87aa8c694a23f50da261d
scipy-1.2.0rc2-cp34-cp34m-manylinux1_i686.whl
897af31662c9d9bfeab4e34a8da8f7b0
scipy-1.2.0rc2-cp34-cp34m-manylinux1_x86_64.whl
c9a0789bb8c2cdf1c1a6b44f7f6958ff scipy-1.2.0rc2-cp34-cp34m-win32.whl
34515d46edd4fc39ac97841c0ffdc535 scipy-1.2.0rc2-cp34-cp34m-win_amd64.whl
cb445d88d28a8df870442f8b75b1f076
scipy-1.2.0rc2-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
3107ddaec0337c99285d7ae7cd517c44
scipy-1.2.0rc2-cp35-cp35m-manylinux1_i686.whl
e801ee5e915ecc47d5800d36a62609ed
scipy-1.2.0rc2-cp35-cp35m-manylinux1_x86_64.whl
3282fa4b3088d18fb5d8a4176911f88c scipy-1.2.0rc2-cp35-cp35m-win32.whl
7178e87b7407f10232c0ec45c53d299b scipy-1.2.0rc2-cp35-cp35m-win_amd64.whl
ad5747f5295b4e7feb98233211d28fba
scipy-1.2.0rc2-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
57b1c8ae77febceee7f1453be210b888
scipy-1.2.0rc2-cp36-cp36m-manylinux1_i686.whl
2eb66820f6806f6b7a7f8e1e66b7ffe6
scipy-1.2.0rc2-cp36-cp36m-manylinux1_x86_64.whl
3cfbf4618f40d79a52caa03eb66eff4b scipy-1.2.0rc2-cp36-cp36m-win32.whl
55a837b1d6bc1bac44f9d02fe71220fb scipy-1.2.0rc2-cp36-cp36m-win_amd64.whl
17bed5da01da6157d33215a8e3d4676f
scipy-1.2.0rc2-cp37-cp37m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
e8fe35c2017709d36f4fcb3ad7499782
scipy-1.2.0rc2-cp37-cp37m-manylinux1_i686.whl
d670611d03945419b272c38ac109c542
scipy-1.2.0rc2-cp37-cp37m-manylinux1_x86_64.whl
cc5a920ee31c68404ccb947a08d8825d scipy-1.2.0rc2-cp37-cp37m-win32.whl
eb3fd215b6a39af8d6ef5f44c5b0b6ce scipy-1.2.0rc2-cp37-cp37m-win_amd64.whl
7fd5310c5b19a6faa32f845bba35208b scipy-1.2.0rc2.tar.gz
1fd898bfbbb2ec2afb155ada32c4ed20 scipy-1.2.0rc2.tar.xz
6022906c840d14272c8000f5d48113be scipy-1.2.0rc2.zip
SHA256
~~~~~~
3b08d5ed06d3518f4cee22536d18b3b33b94265f902a0daf9238ce286e481fb9
scipy-1.2.0rc2-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
3b2a24b91e1570a10f65cdcebf90bd8186738ba25d934be38385f6ad5e56997f
scipy-1.2.0rc2-cp27-cp27m-manylinux1_i686.whl
f3add2b81868de7b3ec147019a6d2a32227dae1366e447ff69ea2744c6d59a3d
scipy-1.2.0rc2-cp27-cp27m-manylinux1_x86_64.whl
b61606b5bb3d4706362d468b3581952cb5f702bd237510d6ef3347a99a6f2ed4
scipy-1.2.0rc2-cp27-cp27m-win32.whl
1498d3b296075b2281524c42706be6d8f41112edda105eb0f5aa3355cee51a21
scipy-1.2.0rc2-cp27-cp27m-win_amd64.whl
01a32acb43729219cf583ba50fd7f77e6ccadd37c6fb835a5a3695c2c51aceb1
scipy-1.2.0rc2-cp27-cp27mu-manylinux1_i686.whl
368fce2d186120a89d6e7ac6ed374fa0418f92c502a54f5b6c462036254496e0
scipy-1.2.0rc2-cp27-cp27mu-manylinux1_x86_64.whl
06a81e1d99f6b02c3f6ef54827dd4bacf53d014988dbc09b954f4124e6c414e1
scipy-1.2.0rc2-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
a859de1cd744f51ffb7870c52b3982e0b78b9ca25f3036a83aae106cd57eb8ab
scipy-1.2.0rc2-cp34-cp34m-manylinux1_i686.whl
e60239d88fd2e094b0369a268b4480750c3c8b37b222dd4da22d53e4a7dc25b3
scipy-1.2.0rc2-cp34-cp34m-manylinux1_x86_64.whl
69ce63646a13b24fb35549dfb4c9daf4ff7dacc964e1720cf8bccb4db54e4aba
scipy-1.2.0rc2-cp34-cp34m-win32.whl
58efa4bfe146e949d1b9a5c772ccb524a8f939e82423fd1129745bcdd029961a
scipy-1.2.0rc2-cp34-cp34m-win_amd64.whl
597c1fded8f49019ed03d1e3648d296d56de8a5d5530ca698addd5707f729048
scipy-1.2.0rc2-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
25735065cfc8a3fc9b6575945c6a130b1dd71d6e096a35288a3c39be87040934
scipy-1.2.0rc2-cp35-cp35m-manylinux1_i686.whl
749262355e689e1f3828cb530496202d9d6cd88a7734f6908266df05e8debad7
scipy-1.2.0rc2-cp35-cp35m-manylinux1_x86_64.whl
2b3d9c56ec70e9be1a8e0a3e483f0bf7f9998d4cd37e9a91d6352ac168de1d3d
scipy-1.2.0rc2-cp35-cp35m-win32.whl
dcd05569a7dd0fc267eadec54d416ab728db3f30447fdc078d536166637a993f
scipy-1.2.0rc2-cp35-cp35m-win_amd64.whl
6957a26d95be2b41dfb6bb0ede0b0d8826827954fdfbe8cf02e7a94fa29243e2
scipy-1.2.0rc2-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
42cd83178ab4ca83b8aa3a0001462895ada61b1ff656c6f6e455a6c9eb8d268a
scipy-1.2.0rc2-cp36-cp36m-manylinux1_i686.whl
e540be156010dac4b89e10b83193e6e700b58113781f20f627a83faafa335d79
scipy-1.2.0rc2-cp36-cp36m-manylinux1_x86_64.whl
a320b6f5cb059c7376ded4a9b464030de972dbf4e58807baf0145e042301a8b0
scipy-1.2.0rc2-cp36-cp36m-win32.whl
37d0352e786d28095df81ced7e68eea2724c6ae66dd2f74c0c59edfbf0427d3e
scipy-1.2.0rc2-cp36-cp36m-win_amd64.whl
6baa1c0242f41dabf3e4ab1be808c793cbd7ed494bff750fafe88546c0015bfb
scipy-1.2.0rc2-cp37-cp37m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
9c2b534675ee8b07b7778d27daafdee06343dc8c96061d45bd646e34663003c1
scipy-1.2.0rc2-cp37-cp37m-manylinux1_i686.whl
8a086063621a531f797188a17b99620b2a3a35153618dac30c8325e8b8eb13ae
scipy-1.2.0rc2-cp37-cp37m-manylinux1_x86_64.whl
d018053c116e9e26334f0a00cf3ef835f725dd168ccb1affeebe2f44c8724003
scipy-1.2.0rc2-cp37-cp37m-win32.whl
1b6b10e7b8b236efb98f51297cd8c73944d1e3f6bec13269b9a22531b298b5eb
scipy-1.2.0rc2-cp37-cp37m-win_amd64.whl
689c5ffb7634560e2a3671f3bac3c2a9926c87bc4d84d8bc7c54ca4695ca3552
scipy-1.2.0rc2.tar.gz
54b3a2f66c36f3cee1803ae843e249c85d814c2d4c4f18452059cdfa28b748af
scipy-1.2.0rc2.tar.xz
6d77e355a517847131c645ccd95e4dcbb6569288eef3ddd8d811cca711cf296f
scipy-1.2.0rc2.zip
-----BEGIN PGP SIGNATURE-----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=doxZ
-----END PGP SIGNATURE-----
Hello All,
I am getting an setuptool error when trying to instal NumPy 1.11.2 on
Python 2.6.
I there a way to download the setuptools to complete the installation?
Here is my build .log
more build.log
Running from numpy source directory.
Traceback (most recent call last):
File "setup.py", line 386, in <module>
setup_package()
File "setup.py", line 363, in setup_package
from setuptools import setup
ImportError: No module named setuptools
Regards,
William James
Analytics - Hybrid Data Management
IBM Cloud, US Distribution Market
Phone: 813-838-7508
From: William James/Tampa/IBM
To: numpy-discussion(a)python.org
Date: 12/05/2018 10:46 AM
Subject: Try to install Numpy 1.11.2, getting setuptool error
Hello All,
I am getting an setuptool error when trying to instal NumPy 1.11.2 on
Python 2.6.
I there a way to download the setuptools to complete the installation?
Here is my build .log
more build.log
Running from numpy source directory.
Traceback (most recent call last):
File "setup.py", line 386, in <module>
setup_package()
File "setup.py", line 363, in setup_package
from setuptools import setup
ImportError: No module named setuptools
Regards,
William James
Analytics - Hybrid Data Management
IBM Cloud, US Distribution Market
Phone: 813-838-7508
Hi All,
Matthew Brett has built OpenBLAS 3.4+ libraries and we are successfully
building wheels with them. There are few remaining PRs/issues marked for
1.16, so I expect to make the 1.16. branch in the next several days. If
there are PRs, especially bug fixes, that you really, really want in 1.16,
now is the time to speak up.
Chuck
