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March 2017
- 42 participants
- 34 discussions
In another thread, there is a discussion of a workshop on "Taking
NumPy In Stride" for PyData Barcelona.
I think it would be great to have something like that at SciPy in
Austin this year.
Jaime can't make it, and I don't think strides are going to fill a
four hour tutorial, so it would be good as part of an advanced numpy
tutorial.
I don't have the bandwidth to put together an entire tutorial, but
maybe someone would like to join forces?
Or if someone is already planning an advanced numpy …
[View More]tutorial, perhaps
I could contribute.
Not much time left to get a proposal in!
-Chris
[View Less]
1
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Announcing Theano 0.9.0
This is a release for a major version, with lots of new features, bug
fixes, and some interface changes (deprecated or potentially misleading
features were removed).
This release is the last major version that features the old GPU back-end (
theano.sandbox.cuda, accessible through device=gpu*). All GPU users are
encouraged to transition to the new GPU back-end, based on libgpuarray (
theano.gpuarray, accessible through device=cuda*). For more information,
see
https://…
[View More]github.com/Theano/Theano/wiki/Converting-to-the-new-gpu-back-end%28…
.
Upgrading to Theano 0.9.0 is recommended for everyone, but you should first
make sure that your code does not raise deprecation warnings with Theano
0.8*. Otherwise either results can change, or warnings may have been turned
into errors.
For those using the bleeding edge version in the git repository, we
encourage you to update to the rel-0.9.0 tag.
What's New
Highlights (since 0.8.0):
- Better Python 3.5 support
- Better numpy 1.12 support
- Conda packages for Mac, Linux and Windows
- Support newer Mac and Windows versions
- More Windows integration:
- Theano scripts (theano-cache and theano-nose) now works on Windows
- Better support for Windows end-lines into C codes
- Support for space in paths on Windows
- Scan improvements:
- More scan optimizations, with faster compilation and gradient
computation
- Support for checkpoint in scan (trade off between speed and memory
usage, useful for long sequences)
- Fixed broadcast checking in scan
- Graphs improvements:
- More numerical stability by default for some graphs
- Better handling of corner cases for theano functions and graph
optimizations
- More graph optimizations with faster compilation and execution
- smaller and more readable graph
- New GPU back-end:
- Removed warp-synchronous programming to get good results with newer
CUDA drivers
- More pooling support on GPU when cuDNN isn't available
- Full support of ignore_border option for pooling
- Inplace storage for shared variables
- float16 storage
- Using PCI bus ID of graphic cards for a better mapping between
theano device number and nvidia-smi number
- Fixed offset error in GpuIncSubtensor
- Less C code compilation
- Added support for bool dtype
- Updated and more complete documentation
- Bug fixes related to merge optimizer and shape inference
- Lot of other bug fixes, crashes fixes and warning improvements
Interface changes:
- Merged CumsumOp/CumprodOp into CumOp
- In MRG module:
- Replaced method multinomial_wo_replacement() with new method
choice()
- Random generator now tries to infer the broadcast pattern of its
output
- New pooling interface
- Pooling parameters can change at run time
- Moved softsign out of sandbox to theano.tensor.nnet.softsign
- Using floatX dtype when converting empty list/tuple
- Roll make the shift be modulo the size of the axis we roll on
- round() default to the same as NumPy: half_to_even
Convolution updates:
- Support of full and half modes for 2D and 3D convolutions including in
conv3d2d
- Allowed pooling of empty batch
- Implement conv2d_transpose convenience function
- Multi-cores convolution and pooling on CPU
- New abstract 3d convolution interface similar to the 2d convolution
interface
- Dilated convolution
GPU:
- cuDNN: support versoin 5.1 and wrap batch normalization (2d and 3d)
and RNN functions
- Multiple-GPU, synchrone update (via platoon, use NCCL)
- Gemv(matrix-vector product) speed up for special shape
- cublas gemv workaround when we reduce on an axis with a dimensions
size of 0
- Warn user that some cuDNN algorithms may produce unexpected results in
certain environments for convolution backward filter operations
- GPUMultinomialFromUniform op now supports multiple dtypes
- Support for MaxAndArgMax for some axis combination
- Support for solve (using cusolver), erfinv and erfcinv
- Implemented GpuAdvancedSubtensor
New features:
- OpFromGraph now allows gradient overriding for every input
- Added Abstract Ops for batch normalization that use cuDNN when
available and pure Theano CPU/GPU alternatives otherwise
- Added gradient of solve, tensorinv (CPU), tensorsolve (CPU),
searchsorted (CPU), DownsampleFactorMaxGradGrad (CPU)
- Added Multinomial Without Replacement
- Allowed partial evaluation of compiled function
- More Rop support
- Indexing support ellipsis: a[..., 3]`, a[1,...,3]
- Added theano.tensor.{tensor5,dtensor5, ...}
- compiledir_format support device
- Added New Theano flag conv.assert_shape to check user-provided shapes
at runtime (for debugging)
- Added new Theano flag cmodule.age_thresh_use
- Added new Theano flag cuda.enabled
- Added new Theano flag nvcc.cudafe to enable faster compilation and
import with old CUDA back-end
- Added new Theano flag print_global_stats to print some global
statistics (time spent) at the end
- Added new Theano flag profiling.ignore_first_call, useful to profile
the new gpu back-end
- remove ProfileMode (use Theano flag profile=True instead)
Others:
- Split op now has C code for CPU and GPU
- theano-cache list now includes compilation times
- Speed up argmax only on GPU (without also needing the max)
- More stack trace in error messages
- Speed up cholesky grad
- log(sum(exp(...))) now get stability optimized
Other more detailed changes:
- Added Jenkins (gpu tests run on pull requests in addition to daily
buildbot)
- Removed old benchmark directory and other old files not used anymore
- Use of 64-bit indexing in sparse ops to allow matrix with more then 231-1
elements
- Allowed more then one output to be an destructive inplace
- More support of negative axis
- Added the keepdims parameter to the norm function
- Make scan gradient more deterministic
Download and Install
You can download Theano from http://pypi.python.org/pypi/Theano
Installation instructions are available at
http://deeplearning.net/software/theano/install.html
Description
Theano is a Python library that allows you to define, optimize, and
efficiently evaluate mathematical expressions involving multi-dimensional
arrays. It is built on top of NumPy. Theano features:
- tight integration with NumPy: a similar interface to NumPy's.
numpy.ndarrays are also used internally in Theano-compiled functions.
- transparent use of a GPU: perform data-intensive computations much
faster than on a CPU.
- efficient symbolic differentiation: Theano can compute derivatives for
functions of one or many inputs.
- speed and stability optimizations: avoid nasty bugs when computing
expressions such as log(1+ exp(x)) for large values of x.
- dynamic C code generation: evaluate expressions faster.
- extensive unit-testing and self-verification: includes tools for
detecting and diagnosing bugs and/or potential problems.
Theano has been powering large-scale computationally intensive scientific
research since 2007, but it is also approachable enough to be used in the
classroom (IFT6266 at the University of Montreal).
Resources
About Theano:
http://deeplearning.net/software/theano/
Theano-related projects:
http://github.com/Theano/Theano/wiki/Related-projects
About NumPy:
http://numpy.scipy.org/
About SciPy:
http://www.scipy.org/
Machine Learning Tutorial with Theano on Deep Architectures:
http://deeplearning.net/tutorial/
Acknowledgments
I would like to thank all contributors of Theano. For this particular
release, many people have helped, notably (in alphabetical order):
- affanv14
- Alexander Matyasko
- Alexandre de Brebisson
- Amjad Almahairi
- Andrés Gottlieb
- Anton Chechetka
- Arnaud Bergeron
- Benjamin Scellier
- Ben Poole
- Bhavishya Pohani
- Bryn Keller
- Caglar
- Carl Thomé
- Cesar Laurent
- Chiheb Trabelsi
- Chinnadhurai Sankar
- Christos Tsirigotis
- Ciyong Chen
- David Bau
- Dimitar Dimitrov
- Evelyn Mitchell
- Fábio Perez
- Faruk Ahmed
- Fei Wang
- Fei Zhan
- Florian Bordes
- Francesco Visin
- Frederic Bastien
- Fuchai
- Gennadiy Tupitsin
- Gijs van Tulder
- Gilles Louppe
- Gokula Krishnan
- Greg Ciccarelli
- gw0 [http://gw.tnode.com/]
- happygds
- Harm de Vries
- He
- hexahedria
- hsintone
- Huan Zhang
- Ilya Kulikov
- Iulian Vlad Serban
- jakirkham
- Jakub Sygnowski
- Jan Schlüter
- Jesse Livezey
- Jonas Degrave
- joncrall
- Kaixhin
- Karthik Karanth
- Kelvin Xu
- Kevin Keraudren
- khaotik
- Kirill Bobyrev
- Kumar Krishna Agrawal
- Kv Manohar
- Liwei Cai
- Lucas Beyer
- Maltimore
- Marc-Alexandre Cote
- Marco
- Marius F. Killinger
- Martin Drawitsch
- Mathieu Germain
- Matt Graham
- Maxim Kochurov
- Micah Bojrab
- Michael Harradon
- Mikhail Korobov
- mockingjamie
- Mohammad Pezeshki
- Morgan Stuart
- Nan Rosemary Ke
- Neil
- Nicolas Ballas
- Nizar Assaf
- Olivier Mastropietro
- Ozan Çağlayan
- p
- Pascal Lamblin
- Pierre Luc Carrier
- RadhikaG
- Ramana Subramanyam
- Ray Donnelly
- Rebecca N. Palmer
- Reyhane Askari
- Rithesh Kumar
- Rizky Luthfianto
- Robin Millette
- Roman Ring
- root
- Ruslana Makovetsky
- Saizheng Zhang
- Samira Ebrahimi Kahou
- Samira Shabanian
- Sander Dieleman
- Sebastin Santy
- Shawn Tan
- Simon Lefrancois
- Sina Honari
- Steven Bocco
- superantichrist
- Taesup (TS) Kim
- texot
- Thomas George
- tillahoffmann
- Tim Cooijmans
- Tim Gasper
- valtron
- Vincent Dumoulin
- Vincent Michalski
- Vitaliy Kurlin
- Wazeer Zulfikar
- wazeerzulfikar
- Wojciech Głogowski
- Xavier Bouthillier
- Yang Zhang
- Yann N. Dauphin
- Yaroslav Ganin
- Ying Zhang
- you-n-g
- Zhouhan LIN
Also, thank you to all NumPy and Scipy developers as Theano builds on their
strengths.
All questions/comments are always welcome on the Theano mailing-lists (
http://deeplearning.net/software/theano/#community )
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1
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Hi All,
I'm pleased to announce the release of NumPy 1.12.1. NumPy 1.12.1 supports
Python 2.7 and 3.4 - 3.6 and fixes bugs and regressions found in NumPy
1.12.0.
In particular, the regression in f2py constant parsing is fixed.
Wheels for Linux, Windows, and OSX can be found on pypi. Archives can be
downloaded
from github <https://github.com/numpy/numpy/releases/tag/v1.12.1>.
*Contributors*
A total of 10 people contributed to this release. People with a "+" by
their
names contributed …
[View More]a patch for the first time.
* Charles Harris
* Eric Wieser
* Greg Young
* Joerg Behrmann +
* John Kirkham
* Julian Taylor
* Marten van Kerkwijk
* Matthew Brett
* Shota Kawabuchi
* Jean Utke +
*Fixes Backported*
* #8483: BUG: Fix wrong future nat warning and equiv type logic error...
* #8489: BUG: Fix wrong masked median for some special cases
* #8490: DOC: Place np.average in inline code
* #8491: TST: Work around isfinite inconsistency on i386
* #8494: BUG: Guard against replacing constants without `'_'` spec in f2py.
* #8524: BUG: Fix mean for float 16 non-array inputs for 1.12
* #8571: BUG: Fix calling python api with error set and minor leaks for...
* #8602: BUG: Make iscomplexobj compatible with custom dtypes again
* #8618: BUG: Fix undefined behaviour induced by bad `__array_wrap__`
* #8648: BUG: Fix `MaskedArray.__setitem__`
* #8659: BUG: PPC64el machines are POWER for Fortran in f2py
* #8665: BUG: Look up methods on MaskedArray in `_frommethod`
* #8674: BUG: Remove extra digit in `binary_repr` at limit
* #8704: BUG: Fix deepcopy regression for empty arrays.
* #8707: BUG: Fix ma.median for empty ndarrays
Cheers,
Chuck
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1
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![](https://secure.gravatar.com/avatar/971399cd3d255dfffb7f1924f4595c05.jpg?s=120&d=mm&r=g)
ANN: xtensor 0.7.1 numpy-style syntax in C++ with bindings to numpy arrays
by Sylvain Corlay March 17, 2017
by Sylvain Corlay March 17, 2017
March 17, 2017
Hi All,
On behalf of the xtensor development team, I am pleased to announce the
releases of
- xtensor 0.7.1 https://github.com/QuantStack/xtensor/
- xtensor-python 0.6.0 https://github.com/QuantStack/xtensor-python/
*What is xtensor?*
xtensor is a C++ library meant for numerical analysis with
multi-dimensional array expressions.
xtensor provides
- an extensible expression system enabling* lazy broadcasting.*
- an API following the idioms of the *C++ standard library*.
-…
[View More] an increasing support of numpy features which you can see in the *NumPy
to xtensor cheat sheet*.
- tools to manipulate array expressions and build upon xtensor.
- *numpy bindings* enabling the inplace use of numpy arrays as xtensor
expressions in C++ extensions.
*What is new in this release?*
In this release, we have added the reducers functionality and the real and
imaginary views for complex arrays. We have increased the performance of
xtensor universal functions.
*Where can I learn more about xtensor?*
Check out the extensive documentation:
http://xtensor.readthedocs.io/en/latest/
Or the numpy to xtensor cheat sheet:
http://xtensor.readthedocs.io/en/latest/numpy.html
Or join us in the chat room:
https://gitter.im/QuantStack/Lobby
Thanks!
Sylvain
>From NumPy to xtensor
Containers
Two container types are provided. xarray (dynamic number of dimensions) and
xtensor (static number of dimensions).
Python 3 - numpyC++ 14 - xtensor
np.array([[3, 4], [5, 6]])
xt::xarray<double>({{3, 4}, {5, 6}})
xt::xtensor<double, 2>({{3, 4}, {5, 6}})
arr.reshape([3, 4]) arr.reshape{{3, 4})
Initializers
Lazy helper functions return tensor expressions. Return types don’t hold
any value and are evaluated upon access or assignment. They can be assigned
to a container or directly used in expressions.
Python 3 - numpyC++ 14 - xtensor
np.linspace(1.0, 10.0, 100) xt::linspace<double>(1.0, 10.0, 100)
np.logspace(2.0, 3.0, 4) xt::logspace<double>(2.0, 3.0, 4)
np.arange(3, 7) xt::arange(3, 7)
np.eye(4) xt::eye(4)
np.zeros([3, 4]) xt::zeros<double>({3, 4})
np.ones([3, 4]) xt::ones<double>({3, 4})
np.meshgrid(x0, x1, x2, indexing='ij') xt::meshgrid(x0, x1, x2)
xtensor’s meshgrid implementation corresponds to numpy’s 'ij' indexing
order.
Broadcasting
xtensor offers lazy numpy-style broadcasting, and universal functions.
Unlike numpy, no copy or temporary variables are created.
Python 3 - numpyC++ 14 - xtensor
a[:, np.newaxis]
a[:5, 1:]
a[5:1:-1, :]
xt::view(a, xt::all(), xt::newaxis())
xt::view(a, xt::range(_, 5), xt::range(1, _))
xt::view(a, xt::range(5, 1, -1), xt::all())
np.broadcast(a, [4, 5, 7]) xt::broadcast(a, {4, 5, 7})
np.vectorize(f) xt::vectorize(f)
a[a > 5] xt::filter(a, a > 5)
a[[0, 1], [0, 0]] xt::index_view(a, {{0, 0}, {1, 0}})
Random
The random module provides simple ways to create random tensor expressions,
lazily.
Python 3 - numpyC++ 14 - xtensor
np.random.randn(10, 10) xt::random::randn<double>({10, 10})
np.random.randint(10, 10) xt::random::randint<int>({10, 10})
np.random.rand(3, 4) xt::random::rand<double>({3, 4})
Concatenation
Concatenating expressions does not allocate memory, it returns a tensor
expression holding closures on the specified arguments.
Python 3 - numpyC++ 14 - xtensor
np.stack([a, b, c], axis=1) xt::stack(xtuple(a, b, c), 1)
np.concatenate([a, b, c], axis=1) xt::concatenate(xtuple(a, b, c), 1)
Diagonal, triangular and flip
In the same spirit as concatenation, the following operations do not
allocate any memory and do not modify the underlying xexpression.
Python 3 - numpyC++ 14 - xtensor
np.diag(a) xt::diag(a)
np.diagonal(a) xt::diagonal(a)
np.triu(a) xt::triu(a)
np.tril(a, k=1) xt::tril(a, 1)
np.flip(a, axis=3) xt::flip(a, 3)
np.flipud(a) xt::flip(a, 0)
np.fliplr(a) xt::flip(a, 1)
Iteration
xtensor follows the idioms of the C++ STL providing iterator pairs to
iterate on arrays in different fashions.
Python 3 - numpyC++ 14 - xtensor
for x in np.nditer(a):
for(auto it=a.xbegin(); it!=a.xend(); ++it)
Iterating with a prescribed broadcasting shape
for(auto it=a.xbegin({3, 4});
it!=a.xend({3, 4}); ++it)
Logical
Logical universal functions are truly lazy. xt::where(condition, a, b) does
not evaluate a where condition is falsy, and it does not evaluate b where
condition is truthy.
Python 3 - numpyC++ 14 - xtensor
np.where(a > 5, a, b) xt::where(a > 5, a, b)
np.where(a > 5) xt::where(a > 5)
np.any(a) xt::any(a)
np.all(a) xt::all(a)
np.logical_and(a, b) a && b
np.logical_or(a, b) a || b
Comparisons
Python 3 - numpyC++ 14 - xtensor
np.equal(a, b) xt::equal(a, b)
np.not_equal(a) xt::not_equal(a)
np.nonzero(a) xt::nonzero(a)
Complex numbers
Functions xt::real and xt::imag respectively return views on the real and
imaginary part of a complex expression. The returned value is an expression
holding a closure on the passed argument.
Python 3 - numpyC++ 14 - xtensor
np.real(a) xt::real(a)
np.imag(a) xt::imag(a)
- The constness and value category (rvalue / lvalue) of real(a) is the
same as that of a. Hence, if a is a non-const lvalue, real(a) is an
non-const lvalue reference, to which one can assign a real expression.
- If a has complex values, the same holds for imag(a). The constness and
value category ofimag(a) is the same as that of a.
- If a has real values, imag(a) returns zeros(a.shape()).
Reducers
Reducers accumulate values of tensor expressions along specified axes. When
no axis is specified, values are accumulated along all axes. Reducers are
lazy, meaning that returned expressons don’t hold any values and are
computed upon access or assigmnent.
Python 3 - numpyC++ 14 - xtensor
np.sum(a, axis=[0, 1]) xt::sum(a, {0, 1})
np.sum(a) xt::sum(a)
np.prod(a, axis=1) xt::prod(a, {1})
np.prod(a) xt::prod(a)
np.mean(a, axis=1) xt::mean(a, {1})
np.mean(a) xt::mean(a)
More generally, one can use the xt::reduce(function, input, axes) which
allows the specification of an arbitrary binary function for the reduction.
The binary function must be cummutative and associative up to rounding
errors.
Mathematical functions
xtensor universal functions are provided for a large set number of
mathematical functions.
*Basic functions:*
Python 3 - numpyC++ 14 - xtensor
np.isnan(a) xt::isnan(a)
np.absolute(a) xt::abs(a)
np.sign(a) xt::sign(a)
np.remainder(a, b) xt::remainder(a, b)
np.clip(a, min, max) xt::clip(a, min, max)
xt::fma(a, b, c)
*Exponential functions:*
Python 3 - numpyC++ 14 - xtensor
np.exp(a) xt::exp(a)
np.expm1(a) xt::expm1(a)
np.log(a) xt::log(a)
np.log1p(a) xt::log1p(a)
*Power functions:*
Python 3 - numpyC++ 14 - xtensor
np.power(a, p) xt::pow(a, b)
np.sqrt(a) xt::sqrt(a)
np.cbrt(a) xt::cbrt(a)
*Trigonometric functions:*
Python 3 - numpyC++ 14 - xtensor
np.sin(a) xt::sin(a)
np.cos(a) xt::cos(a)
np.tan(a) xt::tan(a)
*Hyperbolic functions:*
Python 3 - numpyC++ 14 - xtensor
np.sinh(a) xt::sinh(a)
np.cosh(a) xt::cosh(a)
np.tang(a) xt::tanh(a)
*Error and gamma functions:*
Python 3 - numpyC++ 14 - xtensor
scipy.special.erf(a) xt::erf(a)
scipy.special.gamma(a) xt::tgamma(a)
scipy.special.gammaln(a) xt::lgamma(a)
[View Less]
2
1
Hi,
The scipy.org site is down at the moment, and has been for more than 36 hours:
https://github.com/numpy/numpy/issues/8779#issue-213781439
This has happened before:
https://github.com/scipy/scipy.org/issues/187#issue-186426408
I think it was down for about 24 hours that time.
>From the number of people opening issues or commenting on the
scipy.org website this time, it seems to be causing quite a bit of
disruption.
It seems to me that we would have a much better chances of avoiding
…
[View More]significant down-time, if we switched to hosting the docs on github
pages.
What do y'all think?
Cheers,
Matthew
[View Less]
12
20
Hi,
As numpy often allocates large arrays and one factor in its performance
is faulting memory from the kernel to the process. This has some cost
that is relatively significant. For example in this operation on large
arrays it accounts for 10-15% of the runtime:
import numpy as np
a = np.ones(10000000)
b = np.ones(10000000)
%timeit (a * b)**2 + 3
54.45% ipython umath.so [.] sse2_binary_multiply_DOUBLE
20.43% ipython umath.so [.] DOUBLE_add
16.66% ipython […
[View More]kernel.kallsyms] [k] clear_page
The reason for this is that the glibc memory allocator uses memory
mapping for large allocations instead of reusing already faulted memory.
The reason for this is to return memory back to the system immediately
when it is free to keep the whole system more robust.
This makes a lot of sense in general but not so much for many numerical
applications that often are the only thing running.
But despite if have been shown in an old paper that caching memory in
numpy speeds up many applications, numpys usage is diverse so we
couldn't really diverge from the glibc behaviour.
Until Linux 4.5 added support for madvise(MADV_FREE). This flag of the
madvise syscall tells the kernel that a piece of memory can be reused by
other processes if there is memory pressure. Should another process
claim the memory and the original process want to use it again the
kernel will fault new memory into its place so it behaves exactly as if
it was just freed regularly.
But when no other process claims the memory and the original process
wants to reuse it, the memory do not need to be faulted again.
So effectively this flag allows us to cache memory inside numpy that can
be reused by the rest of the system if required.
Doing gives the expected speedup in the above example.
An issue is that the memory usage of numpy applications will seem to
increase. The memory that is actually free will still show up in the
usual places you look at memory usage. Namely the resident memory usage
of the process in top, /proc etc. The usage will only go down when the
memory is actually needed by other processes.
This probably would break some of the memory profiling tools so probably
we need a switch to disable the caching for the profiling tools to use.
Another concern is that using this functionality is actually the job of
the system memory allocator but I had a look at glibcs allocator and it
does not look like an easy job to make good use of MADV_FREE
retroactively, so I don't expect this to happen anytime soon.
Should it be agreed that caching is worthwhile I would propose a very
simple implementation. We only really need to cache a small handful of
array data pointers for the fast allocate deallocate cycle that appear
in common numpy usage.
For example a small list of maybe 4 pointers storing the 4 largest
recent deallocations. New allocations just pick the first memory block
of sufficient size.
The cache would only be active on systems that support MADV_FREE (which
is linux 4.5 and probably BSD too).
So what do you think of this idea?
cheers,
Julian
[View Less]
3
4
Is https://docs.scipy.org/ being down known issue?
Ryan
--
Ryan May
2
1
Hi everyone,
I wondered how to express a numpy float exactly in terms of format, and
found `%r` quite useful: `float(repr(a)) == a` is guaranteed for Python
`float`s. When trying the same thing with lower-precision Python floats, I
found this identity not quite fulfilled:
```
import numpy
b = numpy.array([1.0 / 3.0], dtype=np.float16)
float(repr(b[0])) - b[0]
Out[12]: -1.9531250000093259e-06
```
Indeed,
```
b
Out[6]: array([ 0.33325195], dtype=float16)
```
```
repr(b[0])
Out[7]: '0.33325'
```
…
[View More]When counting the bits, a float16 should hold 4.8 decimal digits, so
`repr()` seems right. Where does the garbage tail -1.9531250000093259e-06
come from though?
Cheers,
Nico
[View Less]
3
3
Hi,
Has anyone succeeded in building numpy with Clang on Windows? Or,
building any other Python extension with Clang on Windows? I'd love
to hear about how you did it...
Cheers,
Matthew
1
0
On behalf of the Scipy development team I am pleased to announce the
availability of Scipy 0.19.0. This release contains several great new
features and a large number of bug fixes and various improvements, as
detailed in the release notes below.
121 people contributed to this release over the course of seven months.
Thanks to everyone who contributed!
This release requires Python 2.7 or 3.4-3.6 and NumPy 1.8.2 or
greater. Source tarballs and release notes can be found at
https://github.com/…
[View More]scipy/scipy/releases/tag/v0.19.0.
OS X and Linux wheels are available from PyPI. For security-conscious,
the wheels themselves are signed with my GPG key. Additionally, you
can checksum the wheels and verify the checksums with those listed
below or in the README file at
https://github.com/scipy/scipy/releases/tag/v0.19.0.
Cheers,
Evgeni
-----BEGIN PGP SIGNED MESSAGE-----
Hash: SHA256
==========================
SciPy 0.19.0 Release Notes
==========================
.. contents::
SciPy 0.19.0 is the culmination of 7 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. Moreover, our development attention
will now shift to bug-fix releases on the 0.19.x branch, and on adding
new features on the master branch.
This release requires Python 2.7 or 3.4-3.6 and NumPy 1.8.2 or greater.
Highlights of this release include:
- - A unified foreign function interface layer, `scipy.LowLevelCallable`.
- - Cython API for scalar, typed versions of the universal functions from
the `scipy.special` module, via `cimport scipy.special.cython_special`.
New features
============
Foreign function interface improvements
- ---------------------------------------
`scipy.LowLevelCallable` provides a new unified interface for wrapping
low-level compiled callback functions in the Python space. It supports
Cython imported "api" functions, ctypes function pointers, CFFI function
pointers, ``PyCapsules``, Numba jitted functions and more.
See `gh-6509 <https://github.com/scipy/scipy/pull/6509>`_ for details.
`scipy.linalg` improvements
- ---------------------------
The function `scipy.linalg.solve` obtained two more keywords ``assume_a`` and
``transposed``. The underlying LAPACK routines are replaced with "expert"
versions and now can also be used to solve symmetric, hermitian and positive
definite coefficient matrices. Moreover, ill-conditioned matrices now cause
a warning to be emitted with the estimated condition number information. Old
``sym_pos`` keyword is kept for backwards compatibility reasons however it
is identical to using ``assume_a='pos'``. Moreover, the ``debug`` keyword,
which had no function but only printing the ``overwrite_<a, b>`` values, is
deprecated.
The function `scipy.linalg.matrix_balance` was added to perform the so-called
matrix balancing using the LAPACK xGEBAL routine family. This can be used to
approximately equate the row and column norms through diagonal similarity
transformations.
The functions `scipy.linalg.solve_continuous_are` and
`scipy.linalg.solve_discrete_are` have numerically more stable algorithms.
These functions can also solve generalized algebraic matrix Riccati equations.
Moreover, both gained a ``balanced`` keyword to turn balancing on and off.
`scipy.spatial` improvements
- ----------------------------
`scipy.spatial.SphericalVoronoi.sort_vertices_of_regions` has been re-written in
Cython to improve performance.
`scipy.spatial.SphericalVoronoi` can handle > 200 k points (at least 10 million)
and has improved performance.
The function `scipy.spatial.distance.directed_hausdorff` was
added to calculate the directed Hausdorff distance.
``count_neighbors`` method of `scipy.spatial.cKDTree` gained an ability to
perform weighted pair counting via the new keywords ``weights`` and
``cumulative``. See `gh-5647 <https://github.com/scipy/scipy/pull/5647>`_ for
details.
`scipy.spatial.distance.pdist` and `scipy.spatial.distance.cdist` now support
non-double custom metrics.
`scipy.ndimage` improvements
- ----------------------------
The callback function C API supports PyCapsules in Python 2.7
Multidimensional filters now allow having different extrapolation modes for
different axes.
`scipy.optimize` improvements
- -----------------------------
The `scipy.optimize.basinhopping` global minimizer obtained a new keyword,
`seed`, which can be used to seed the random number generator and obtain
repeatable minimizations.
The keyword `sigma` in `scipy.optimize.curve_fit` was overloaded to also accept
the covariance matrix of errors in the data.
`scipy.signal` improvements
- ---------------------------
The function `scipy.signal.correlate` and `scipy.signal.convolve` have a new
optional parameter `method`. The default value of `auto` estimates the fastest
of two computation methods, the direct approach and the Fourier transform
approach.
A new function has been added to choose the convolution/correlation method,
`scipy.signal.choose_conv_method` which may be appropriate if convolutions or
correlations are performed on many arrays of the same size.
New functions have been added to calculate complex short time fourier
transforms of an input signal, and to invert the transform to recover the
original signal: `scipy.signal.stft` and `scipy.signal.istft`. This
implementation also fixes the previously incorrect ouput of
`scipy.signal.spectrogram` when complex output data were requested.
The function `scipy.signal.sosfreqz` was added to compute the frequency
response from second-order sections.
The function `scipy.signal.unit_impulse` was added to conveniently
generate an impulse function.
The function `scipy.signal.iirnotch` was added to design second-order
IIR notch filters that can be used to remove a frequency component from
a signal. The dual function `scipy.signal.iirpeak` was added to
compute the coefficients of a second-order IIR peak (resonant) filter.
The function `scipy.signal.minimum_phase` was added to convert linear-phase
FIR filters to minimum phase.
The functions `scipy.signal.upfirdn` and `scipy.signal.resample_poly` are now
substantially faster when operating on some n-dimensional arrays when n > 1.
The largest reduction in computation time is realized in cases where the size
of the array is small (<1k samples or so) along the axis to be filtered.
`scipy.fftpack` improvements
- ----------------------------
Fast Fourier transform routines now accept `np.float16` inputs and upcast
them to `np.float32`. Previously, they would raise an error.
`scipy.cluster` improvements
- ----------------------------
Methods ``"centroid"`` and ``"median"`` of `scipy.cluster.hierarchy.linkage`
have been significantly sped up. Long-standing issues with using ``linkage`` on
large input data (over 16 GB) have been resolved.
`scipy.sparse` improvements
- ---------------------------
The functions `scipy.sparse.save_npz` and `scipy.sparse.load_npz` were added,
providing simple serialization for some sparse formats.
The `prune` method of classes `bsr_matrix`, `csc_matrix`, and `csr_matrix`
was updated to reallocate backing arrays under certain conditions, reducing
memory usage.
The methods `argmin` and `argmax` were added to classes `coo_matrix`,
`csc_matrix`, `csr_matrix`, and `bsr_matrix`.
New function `scipy.sparse.csgraph.structural_rank` computes the structural
rank of a graph with a given sparsity pattern.
New function `scipy.sparse.linalg.spsolve_triangular` solves a sparse linear
system with a triangular left hand side matrix.
`scipy.special` improvements
- ----------------------------
Scalar, typed versions of universal functions from `scipy.special` are available
in the Cython space via ``cimport`` from the new module
`scipy.special.cython_special`. These scalar functions can be expected to be
significantly faster then the universal functions for scalar arguments. See
the `scipy.special` tutorial for details.
Better control over special-function errors is offered by the
functions `scipy.special.geterr` and `scipy.special.seterr` and the
context manager `scipy.special.errstate`.
The names of orthogonal polynomial root functions have been changed to
be consistent with other functions relating to orthogonal
polynomials. For example, `scipy.special.j_roots` has been renamed
`scipy.special.roots_jacobi` for consistency with the related
functions `scipy.special.jacobi` and `scipy.special.eval_jacobi`. To
preserve back-compatibility the old names have been left as aliases.
Wright Omega function is implemented as `scipy.special.wrightomega`.
`scipy.stats` improvements
- --------------------------
The function `scipy.stats.weightedtau` was added. It provides a weighted
version of Kendall's tau.
New class `scipy.stats.multinomial` implements the multinomial distribution.
New class `scipy.stats.rv_histogram` constructs a continuous univariate
distribution with a piecewise linear CDF from a binned data sample.
New class `scipy.stats.argus` implements the Argus distribution.
`scipy.interpolate` improvements
- --------------------------------
New class `scipy.interpolate.BSpline` represents splines. ``BSpline`` objects
contain knots and coefficients and can evaluate the spline. The format is
consistent with FITPACK, so that one can do, for example::
>>> t, c, k = splrep(x, y, s=0)
>>> spl = BSpline(t, c, k)
>>> np.allclose(spl(x), y)
``spl*`` functions, `scipy.interpolate.splev`, `scipy.interpolate.splint`,
`scipy.interpolate.splder` and `scipy.interpolate.splantider`, accept both
``BSpline`` objects and ``(t, c, k)`` tuples for backwards compatibility.
For multidimensional splines, ``c.ndim > 1``, ``BSpline`` objects are consistent
with piecewise polynomials, `scipy.interpolate.PPoly`. This means that
``BSpline`` objects are not immediately consistent with
`scipy.interpolate.splprep`, and one *cannot* do
``>>> BSpline(*splprep([x, y])[0])``. Consult the `scipy.interpolate` test suite
for examples of the precise equivalence.
In new code, prefer using ``scipy.interpolate.BSpline`` objects instead of
manipulating ``(t, c, k)`` tuples directly.
New function `scipy.interpolate.make_interp_spline` constructs an interpolating
spline given data points and boundary conditions.
New function `scipy.interpolate.make_lsq_spline` constructs a least-squares
spline approximation given data points.
`scipy.integrate` improvements
- ------------------------------
Now `scipy.integrate.fixed_quad` supports vector-valued functions.
Deprecated features
===================
`scipy.interpolate.splmake`, `scipy.interpolate.spleval` and
`scipy.interpolate.spline` are deprecated. The format used by `splmake/spleval`
was inconsistent with `splrep/splev` which was confusing to users.
`scipy.special.errprint` is deprecated. Improved functionality is
available in `scipy.special.seterr`.
calling `scipy.spatial.distance.pdist` or `scipy.spatial.distance.cdist` with
arguments not needed by the chosen metric is deprecated. Also, metrics
`"old_cosine"` and `"old_cos"` are deprecated.
Backwards incompatible changes
==============================
The deprecated ``scipy.weave`` submodule was removed.
`scipy.spatial.distance.squareform` now returns arrays of the same dtype as
the input, instead of always float64.
`scipy.special.errprint` now returns a boolean.
The function `scipy.signal.find_peaks_cwt` now returns an array instead of
a list.
`scipy.stats.kendalltau` now computes the correct p-value in case the
input contains ties. The p-value is also identical to that computed by
`scipy.stats.mstats.kendalltau` and by R. If the input does not
contain ties there is no change w.r.t. the previous implementation.
The function `scipy.linalg.block_diag` will not ignore zero-sized
matrices anymore.
Instead it will insert rows or columns of zeros of the appropriate size.
See gh-4908 for more details.
Other changes
=============
SciPy wheels will now report their dependency on ``numpy`` on all platforms.
This change was made because Numpy wheels are available, and because the pip
upgrade behavior is finally changing for the better (use
``--upgrade-strategy=only-if-needed`` for ``pip >= 8.2``; that behavior will
become the default in the next major version of ``pip``).
Numerical values returned by `scipy.interpolate.interp1d` with ``kind="cubic"``
and ``"quadratic"`` may change relative to previous scipy versions. If your
code depended on specific numeric values (i.e., on implementation
details of the interpolators), you may want to double-check your results.
Authors
=======
* @endolith
* Max Argus +
* Hervé Audren
* Alessandro Pietro Bardelli +
* Michael Benfield +
* Felix Berkenkamp
* Matthew Brett
* Per Brodtkorb
* Evgeni Burovski
* Pierre de Buyl
* CJ Carey
* Brandon Carter +
* Tim Cera
* Klesk Chonkin
* Christian Häggström +
* Luca Citi
* Peadar Coyle +
* Daniel da Silva +
* Greg Dooper +
* John Draper +
* drlvk +
* David Ellis +
* Yu Feng
* Baptiste Fontaine +
* Jed Frey +
* Siddhartha Gandhi +
* Wim Glenn +
* Akash Goel +
* Christoph Gohlke
* Ralf Gommers
* Alexander Goncearenco +
* Richard Gowers +
* Alex Griffing
* Radoslaw Guzinski +
* Charles Harris
* Callum Jacob Hays +
* Ian Henriksen
* Randy Heydon +
* Lindsey Hiltner +
* Gerrit Holl +
* Hiroki IKEDA +
* jfinkels +
* Mher Kazandjian +
* Thomas Keck +
* keuj6 +
* Kornel Kielczewski +
* Sergey B Kirpichev +
* Vasily Kokorev +
* Eric Larson
* Denis Laxalde
* Gregory R. Lee
* Josh Lefler +
* Julien Lhermitte +
* Evan Limanto +
* Jin-Guo Liu +
* Nikolay Mayorov
* Geordie McBain +
* Josue Melka +
* Matthieu Melot
* michaelvmartin15 +
* Surhud More +
* Brett M. Morris +
* Chris Mutel +
* Paul Nation
* Andrew Nelson
* David Nicholson +
* Aaron Nielsen +
* Joel Nothman
* nrnrk +
* Juan Nunez-Iglesias
* Mikhail Pak +
* Gavin Parnaby +
* Thomas Pingel +
* Ilhan Polat +
* Aman Pratik +
* Sebastian Pucilowski
* Ted Pudlik
* puenka +
* Eric Quintero
* Tyler Reddy
* Joscha Reimer
* Antonio Horta Ribeiro +
* Edward Richards +
* Roman Ring +
* Rafael Rossi +
* Colm Ryan +
* Sami Salonen +
* Alvaro Sanchez-Gonzalez +
* Johannes Schmitz
* Kari Schoonbee
* Yurii Shevchuk +
* Jonathan Siebert +
* Jonathan Tammo Siebert +
* Scott Sievert +
* Sourav Singh
* Byron Smith +
* Srikiran +
* Samuel St-Jean +
* Yoni Teitelbaum +
* Bhavika Tekwani
* Martin Thoma
* timbalam +
* Svend Vanderveken +
* Sebastiano Vigna +
* Aditya Vijaykumar +
* Santi Villalba +
* Ze Vinicius
* Pauli Virtanen
* Matteo Visconti
* Yusuke Watanabe +
* Warren Weckesser
* Phillip Weinberg +
* Nils Werner
* Jakub Wilk
* Josh Wilson
* wirew0rm +
* David Wolever +
* Nathan Woods
* ybeltukov +
* G Young
* Evgeny Zhurko +
A total of 121 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 0.19.0
- ------------------------
- - `#1767 <https://github.com/scipy/scipy/issues/1767>`__: Function
definitions in __fitpack.h should be moved. (Trac #1240)
- - `#1774 <https://github.com/scipy/scipy/issues/1774>`__: _kmeans
chokes on large thresholds (Trac #1247)
- - `#2089 <https://github.com/scipy/scipy/issues/2089>`__: Integer
overflows cause segfault in linkage function with large...
- - `#2190 <https://github.com/scipy/scipy/issues/2190>`__: Are
odd-length window functions supposed to be always symmetrical?...
- - `#2251 <https://github.com/scipy/scipy/issues/2251>`__:
solve_discrete_are in scipy.linalg does (sometimes) not solve...
- - `#2580 <https://github.com/scipy/scipy/issues/2580>`__:
scipy.interpolate.UnivariateSpline (or a new superclass of it)...
- - `#2592 <https://github.com/scipy/scipy/issues/2592>`__:
scipy.stats.anderson assumes gumbel_l
- - `#3054 <https://github.com/scipy/scipy/issues/3054>`__:
scipy.linalg.eig does not handle infinite eigenvalues
- - `#3160 <https://github.com/scipy/scipy/issues/3160>`__:
multinomial pmf / logpmf
- - `#3904 <https://github.com/scipy/scipy/issues/3904>`__:
scipy.special.ellipj dn wrong values at quarter period
- - `#4044 <https://github.com/scipy/scipy/issues/4044>`__:
Inconsistent code book initialization in kmeans
- - `#4234 <https://github.com/scipy/scipy/issues/4234>`__:
scipy.signal.flattop documentation doesn't list a source for...
- - `#4831 <https://github.com/scipy/scipy/issues/4831>`__: Bugs in C
code in __quadpack.h
- - `#4908 <https://github.com/scipy/scipy/issues/4908>`__: bug:
unnessesary validity check for block dimension in
scipy.sparse.block_diag
- - `#4917 <https://github.com/scipy/scipy/issues/4917>`__: BUG:
indexing error for sparse matrix with ix_
- - `#4938 <https://github.com/scipy/scipy/issues/4938>`__: Docs on
extending ndimage need to be updated.
- - `#5056 <https://github.com/scipy/scipy/issues/5056>`__: sparse
matrix element-wise multiplying dense matrix returns dense...
- - `#5337 <https://github.com/scipy/scipy/issues/5337>`__: Formula in
documentation for correlate is wrong
- - `#5537 <https://github.com/scipy/scipy/issues/5537>`__: use
OrderedDict in io.netcdf
- - `#5750 <https://github.com/scipy/scipy/issues/5750>`__: [doc]
missing data index value in KDTree, cKDTree
- - `#5755 <https://github.com/scipy/scipy/issues/5755>`__: p-value
computation in scipy.stats.kendalltau() in broken in...
- - `#5757 <https://github.com/scipy/scipy/issues/5757>`__: BUG:
Incorrect complex output of signal.spectrogram
- - `#5964 <https://github.com/scipy/scipy/issues/5964>`__: ENH:
expose scalar versions of scipy.special functions to cython
- - `#6107 <https://github.com/scipy/scipy/issues/6107>`__:
scipy.cluster.hierarchy.single segmentation fault with 2**16...
- - `#6278 <https://github.com/scipy/scipy/issues/6278>`__:
optimize.basinhopping should take a RandomState object
- - `#6296 <https://github.com/scipy/scipy/issues/6296>`__:
InterpolatedUnivariateSpline: check_finite fails when w is unspecified
- - `#6306 <https://github.com/scipy/scipy/issues/6306>`__:
Anderson-Darling bad results
- - `#6314 <https://github.com/scipy/scipy/issues/6314>`__:
scipy.stats.kendaltau() p value not in agreement with R, SPSS...
- - `#6340 <https://github.com/scipy/scipy/issues/6340>`__: Curve_fit
bounds and maxfev
- - `#6377 <https://github.com/scipy/scipy/issues/6377>`__:
expm_multiply, complex matrices not working using start,stop,ect...
- - `#6382 <https://github.com/scipy/scipy/issues/6382>`__:
optimize.differential_evolution stopping criterion has unintuitive...
- - `#6391 <https://github.com/scipy/scipy/issues/6391>`__: Global
Benchmarking times out at 600s.
- - `#6397 <https://github.com/scipy/scipy/issues/6397>`__: mmwrite
errors with large (but still 64-bit) integers
- - `#6413 <https://github.com/scipy/scipy/issues/6413>`__:
scipy.stats.dirichlet computes multivariate gaussian differential...
- - `#6428 <https://github.com/scipy/scipy/issues/6428>`__:
scipy.stats.mstats.mode modifies input
- - `#6440 <https://github.com/scipy/scipy/issues/6440>`__: Figure out
ABI break policy for scipy.special Cython API
- - `#6441 <https://github.com/scipy/scipy/issues/6441>`__: Using
Qhull for halfspace intersection : segfault
- - `#6442 <https://github.com/scipy/scipy/issues/6442>`__:
scipy.spatial : In incremental mode volume is not recomputed
- - `#6451 <https://github.com/scipy/scipy/issues/6451>`__:
Documentation for scipy.cluster.hierarchy.to_tree is confusing...
- - `#6490 <https://github.com/scipy/scipy/issues/6490>`__: interp1d
(kind=zero) returns wrong value for rightmost interpolation...
- - `#6521 <https://github.com/scipy/scipy/issues/6521>`__:
scipy.stats.entropy does *not* calculate the KL divergence
- - `#6530 <https://github.com/scipy/scipy/issues/6530>`__:
scipy.stats.spearmanr unexpected NaN handling
- - `#6541 <https://github.com/scipy/scipy/issues/6541>`__: Test
runner does not run scipy._lib/tests?
- - `#6552 <https://github.com/scipy/scipy/issues/6552>`__: BUG:
misc.bytescale returns unexpected results when using cmin/cmax...
- - `#6556 <https://github.com/scipy/scipy/issues/6556>`__:
RectSphereBivariateSpline(u, v, r) fails if min(v) >= pi
- - `#6559 <https://github.com/scipy/scipy/issues/6559>`__:
Differential_evolution maxiter causing memory overflow
- - `#6565 <https://github.com/scipy/scipy/issues/6565>`__: Coverage
of spectral functions could be improved
- - `#6628 <https://github.com/scipy/scipy/issues/6628>`__: Incorrect
parameter name in binomial documentation
- - `#6634 <https://github.com/scipy/scipy/issues/6634>`__: Expose
LAPACK's xGESVX family for linalg.solve ill-conditioned...
- - `#6657 <https://github.com/scipy/scipy/issues/6657>`__: Confusing
documentation for `scipy.special.sph_harm`
- - `#6676 <https://github.com/scipy/scipy/issues/6676>`__: optimize:
Incorrect size of Jacobian returned by `minimize(...,...
- - `#6681 <https://github.com/scipy/scipy/issues/6681>`__: add a new
context manager to wrap `scipy.special.seterr`
- - `#6700 <https://github.com/scipy/scipy/issues/6700>`__: BUG:
scipy.io.wavfile.read stays in infinite loop, warns on wav...
- - `#6721 <https://github.com/scipy/scipy/issues/6721>`__:
scipy.special.chebyt(N) throw a 'TypeError' when N > 64
- - `#6727 <https://github.com/scipy/scipy/issues/6727>`__:
Documentation for scipy.stats.norm.fit is incorrect
- - `#6764 <https://github.com/scipy/scipy/issues/6764>`__:
Documentation for scipy.spatial.Delaunay is partially incorrect
- - `#6811 <https://github.com/scipy/scipy/issues/6811>`__:
scipy.spatial.SphericalVoronoi fails for large number of points
- - `#6841 <https://github.com/scipy/scipy/issues/6841>`__: spearmanr
fails when nan_policy='omit' is set
- - `#6869 <https://github.com/scipy/scipy/issues/6869>`__: Currently
in gaussian_kde, the logpdf function is calculated...
- - `#6875 <https://github.com/scipy/scipy/issues/6875>`__: SLSQP
inconsistent handling of invalid bounds
- - `#6876 <https://github.com/scipy/scipy/issues/6876>`__: Python
stopped working (Segfault?) with minimum/maximum filter...
- - `#6889 <https://github.com/scipy/scipy/issues/6889>`__: dblquad
gives different results under scipy 0.17.1 and 0.18.1
- - `#6898 <https://github.com/scipy/scipy/issues/6898>`__: BUG:
dblquad ignores error tolerances
- - `#6901 <https://github.com/scipy/scipy/issues/6901>`__: Solving
sparse linear systems in CSR format with complex values
- - `#6903 <https://github.com/scipy/scipy/issues/6903>`__: issue in
spatial.distance.pdist docstring
- - `#6917 <https://github.com/scipy/scipy/issues/6917>`__: Problem in
passing drop_rule to scipy.sparse.linalg.spilu
- - `#6926 <https://github.com/scipy/scipy/issues/6926>`__: signature
mismatches for LowLevelCallable
- - `#6961 <https://github.com/scipy/scipy/issues/6961>`__: Scipy
contains shebang pointing to /usr/bin/python and /bin/bash...
- - `#6972 <https://github.com/scipy/scipy/issues/6972>`__: BUG:
special: `generate_ufuncs.py` is broken
- - `#6984 <https://github.com/scipy/scipy/issues/6984>`__: Assert
raises test failure for test_ill_condition_warning
- - `#6990 <https://github.com/scipy/scipy/issues/6990>`__: BUG:
sparse: Bad documentation of the `k` argument in `sparse.linalg.eigs`
- - `#6991 <https://github.com/scipy/scipy/issues/6991>`__: Division
by zero in linregress()
- - `#7011 <https://github.com/scipy/scipy/issues/7011>`__: possible
speed improvment in rv_continuous.fit()
- - `#7015 <https://github.com/scipy/scipy/issues/7015>`__: Test
failure with Python 3.5 and numpy master
- - `#7055 <https://github.com/scipy/scipy/issues/7055>`__: SciPy
0.19.0rc1 test errors and failures on Windows
- - `#7096 <https://github.com/scipy/scipy/issues/7096>`__: macOS test
failues for test_solve_continuous_are
- - `#7100 <https://github.com/scipy/scipy/issues/7100>`__:
test_distance.test_Xdist_deprecated_args test error in 0.19.0rc2
Pull requests for 0.19.0
- ------------------------
- - `#2908 <https://github.com/scipy/scipy/pull/2908>`__: Scipy 1.0 Roadmap
- - `#3174 <https://github.com/scipy/scipy/pull/3174>`__: add b-splines
- - `#4606 <https://github.com/scipy/scipy/pull/4606>`__: ENH: Add a
unit impulse waveform function
- - `#5608 <https://github.com/scipy/scipy/pull/5608>`__: Adds keyword
argument to choose faster convolution method
- - `#5647 <https://github.com/scipy/scipy/pull/5647>`__: ENH: Faster
count_neighour in cKDTree / + weighted input data
- - `#6021 <https://github.com/scipy/scipy/pull/6021>`__: Netcdf append
- - `#6058 <https://github.com/scipy/scipy/pull/6058>`__: ENH:
scipy.signal - Add stft and istft
- - `#6059 <https://github.com/scipy/scipy/pull/6059>`__: ENH: More
accurate signal.freqresp for zpk systems
- - `#6195 <https://github.com/scipy/scipy/pull/6195>`__: ENH: Cython
interface for special
- - `#6234 <https://github.com/scipy/scipy/pull/6234>`__: DOC: Fixed a
typo in ward() help
- - `#6261 <https://github.com/scipy/scipy/pull/6261>`__: ENH: add
docstring and clean up code for signal.normalize
- - `#6270 <https://github.com/scipy/scipy/pull/6270>`__: MAINT:
special: add tests for cdflib
- - `#6271 <https://github.com/scipy/scipy/pull/6271>`__: Fix for
scipy.cluster.hierarchy.is_isomorphic
- - `#6273 <https://github.com/scipy/scipy/pull/6273>`__: optimize:
rewrite while loops as for loops
- - `#6279 <https://github.com/scipy/scipy/pull/6279>`__: MAINT: Bessel tweaks
- - `#6291 <https://github.com/scipy/scipy/pull/6291>`__: Fixes
gh-6219: remove runtime warning from genextreme distribution
- - `#6294 <https://github.com/scipy/scipy/pull/6294>`__: STY: Some
PEP8 and cleaning up imports in stats/_continuous_distns.py
- - `#6297 <https://github.com/scipy/scipy/pull/6297>`__: Clarify docs
in misc/__init__.py
- - `#6300 <https://github.com/scipy/scipy/pull/6300>`__: ENH: sparse:
Loosen input validation for `diags` with empty inputs
- - `#6301 <https://github.com/scipy/scipy/pull/6301>`__: BUG:
standardizes check_finite behavior re optional weights,...
- - `#6303 <https://github.com/scipy/scipy/pull/6303>`__: Fixing
example in _lazyselect docstring.
- - `#6307 <https://github.com/scipy/scipy/pull/6307>`__: MAINT: more
improvements to gammainc/gammaincc
- - `#6308 <https://github.com/scipy/scipy/pull/6308>`__: Clarified
documentation of hypergeometric distribution.
- - `#6309 <https://github.com/scipy/scipy/pull/6309>`__: BUG: stats:
Improve calculation of the Anderson-Darling statistic.
- - `#6315 <https://github.com/scipy/scipy/pull/6315>`__: ENH:
Descending order of x in PPoly
- - `#6317 <https://github.com/scipy/scipy/pull/6317>`__: ENH: stats:
Add support for nan_policy to stats.median_test
- - `#6321 <https://github.com/scipy/scipy/pull/6321>`__: TST: fix a
typo in test name
- - `#6328 <https://github.com/scipy/scipy/pull/6328>`__: ENH: sosfreqz
- - `#6335 <https://github.com/scipy/scipy/pull/6335>`__: Define
LinregressResult outside of linregress
- - `#6337 <https://github.com/scipy/scipy/pull/6337>`__: In anderson
test, added support for right skewed gumbel distribution.
- - `#6341 <https://github.com/scipy/scipy/pull/6341>`__: Accept
several spellings for the curve_fit max number of function...
- - `#6342 <https://github.com/scipy/scipy/pull/6342>`__: DOC:
cluster: clarify hierarchy.linkage usage
- - `#6352 <https://github.com/scipy/scipy/pull/6352>`__: DOC: removed
brentq from its own 'see also'
- - `#6362 <https://github.com/scipy/scipy/pull/6362>`__: ENH: stats:
Use explicit formulas for sf, logsf, etc in weibull...
- - `#6369 <https://github.com/scipy/scipy/pull/6369>`__: MAINT:
special: add a comment to hyp0f1_complex
- - `#6375 <https://github.com/scipy/scipy/pull/6375>`__: Added the
multinomial distribution.
- - `#6387 <https://github.com/scipy/scipy/pull/6387>`__: MAINT:
special: improve accuracy of ellipj's `dn` at quarter...
- - `#6388 <https://github.com/scipy/scipy/pull/6388>`__:
BenchmarkGlobal - getting it to work in Python3
- - `#6394 <https://github.com/scipy/scipy/pull/6394>`__: ENH:
scipy.sparse: add save and load functions for sparse matrices
- - `#6400 <https://github.com/scipy/scipy/pull/6400>`__: MAINT: moves
global benchmark run from setup_cache to track_all
- - `#6403 <https://github.com/scipy/scipy/pull/6403>`__: ENH: seed
kwd for basinhopping. Closes #6278
- - `#6404 <https://github.com/scipy/scipy/pull/6404>`__: ENH: signal:
added irrnotch and iirpeak functions.
- - `#6406 <https://github.com/scipy/scipy/pull/6406>`__: ENH:
special: extend `sici`/`shichi` to complex arguments
- - `#6407 <https://github.com/scipy/scipy/pull/6407>`__: ENH: Window
functions should not accept non-integer or negative...
- - `#6408 <https://github.com/scipy/scipy/pull/6408>`__: MAINT:
_differentialevolution now uses _lib._util.check_random_state
- - `#6427 <https://github.com/scipy/scipy/pull/6427>`__: MAINT: Fix
gmpy build & test that mpmath uses gmpy
- - `#6439 <https://github.com/scipy/scipy/pull/6439>`__: MAINT:
ndimage: update callback function c api
- - `#6443 <https://github.com/scipy/scipy/pull/6443>`__: BUG: Fix
volume computation in incremental mode
- - `#6447 <https://github.com/scipy/scipy/pull/6447>`__: Fixes issue
#6413 - Minor documentation fix in the entropy function...
- - `#6448 <https://github.com/scipy/scipy/pull/6448>`__: ENH: Add
halfspace mode to Qhull
- - `#6449 <https://github.com/scipy/scipy/pull/6449>`__: ENH: rtol
and atol for differential_evolution termination fixes...
- - `#6453 <https://github.com/scipy/scipy/pull/6453>`__: DOC: Add
some See Also links between similar functions
- - `#6454 <https://github.com/scipy/scipy/pull/6454>`__: DOC: linalg:
clarify callable signature in `ordqz`
- - `#6457 <https://github.com/scipy/scipy/pull/6457>`__: ENH:
spatial: enable non-double dtypes in squareform
- - `#6459 <https://github.com/scipy/scipy/pull/6459>`__: BUG: Complex
matrices not handled correctly by expm_multiply...
- - `#6465 <https://github.com/scipy/scipy/pull/6465>`__: TST DOC
Window docs, tests, etc.
- - `#6469 <https://github.com/scipy/scipy/pull/6469>`__: ENH: linalg:
better handling of infinite eigenvalues in `eig`/`eigvals`
- - `#6475 <https://github.com/scipy/scipy/pull/6475>`__: DOC: calling
interp1d/interp2d with NaNs is undefined
- - `#6477 <https://github.com/scipy/scipy/pull/6477>`__: Document
magic numbers in optimize.py
- - `#6481 <https://github.com/scipy/scipy/pull/6481>`__: TST: Supress
some warnings from test_windows
- - `#6485 <https://github.com/scipy/scipy/pull/6485>`__: DOC:
spatial: correct typo in procrustes
- - `#6487 <https://github.com/scipy/scipy/pull/6487>`__: Fix
Bray-Curtis formula in pdist docstring
- - `#6493 <https://github.com/scipy/scipy/pull/6493>`__: ENH: Add
covariance functionality to scipy.optimize.curve_fit
- - `#6494 <https://github.com/scipy/scipy/pull/6494>`__: ENH: stats:
Use log1p() to improve some calculations.
- - `#6495 <https://github.com/scipy/scipy/pull/6495>`__: BUG: Use MST
algorithm instead of SLINK for single linkage clustering
- - `#6497 <https://github.com/scipy/scipy/pull/6497>`__: MRG: Add
minimum_phase filter function
- - `#6505 <https://github.com/scipy/scipy/pull/6505>`__: reset
scipy.signal.resample window shape to 1-D
- - `#6507 <https://github.com/scipy/scipy/pull/6507>`__: BUG:
linkage: Raise exception if y contains non-finite elements
- - `#6509 <https://github.com/scipy/scipy/pull/6509>`__: ENH: _lib:
add common machinery for low-level callback functions
- - `#6520 <https://github.com/scipy/scipy/pull/6520>`__:
scipy.sparse.base.__mul__ non-numpy/scipy objects with 'shape'...
- - `#6522 <https://github.com/scipy/scipy/pull/6522>`__: Replace
kl_div by rel_entr in entropy
- - `#6524 <https://github.com/scipy/scipy/pull/6524>`__: DOC: add
next_fast_len to list of functions
- - `#6527 <https://github.com/scipy/scipy/pull/6527>`__: DOC: Release
notes to reflect the new covariance feature in optimize.curve_fit
- - `#6532 <https://github.com/scipy/scipy/pull/6532>`__: ENH:
Simplify _cos_win, document it, add symmetric/periodic arg
- - `#6535 <https://github.com/scipy/scipy/pull/6535>`__: MAINT:
sparse.csgraph: updating old cython loops
- - `#6540 <https://github.com/scipy/scipy/pull/6540>`__: DOC: add to
documentation of orthogonal polynomials
- - `#6544 <https://github.com/scipy/scipy/pull/6544>`__: TST: Ensure
tests for scipy._lib are run by scipy.test()
- - `#6546 <https://github.com/scipy/scipy/pull/6546>`__: updated
docstring of stats.linregress
- - `#6553 <https://github.com/scipy/scipy/pull/6553>`__: commited
changes that I originally submitted for scipy.signal.cspline…
- - `#6561 <https://github.com/scipy/scipy/pull/6561>`__: BUG: modify
signal.find_peaks_cwt() to return array and accept...
- - `#6562 <https://github.com/scipy/scipy/pull/6562>`__: DOC:
Negative binomial distribution clarification
- - `#6563 <https://github.com/scipy/scipy/pull/6563>`__: MAINT: be
more liberal in requiring numpy
- - `#6567 <https://github.com/scipy/scipy/pull/6567>`__: MAINT: use
xrange for iteration in differential_evolution fixes...
- - `#6572 <https://github.com/scipy/scipy/pull/6572>`__: BUG:
"sp.linalg.solve_discrete_are" fails for random data
- - `#6578 <https://github.com/scipy/scipy/pull/6578>`__: BUG: misc:
allow both cmin/cmax and low/high params in bytescale
- - `#6581 <https://github.com/scipy/scipy/pull/6581>`__: Fix some
unfortunate typos
- - `#6582 <https://github.com/scipy/scipy/pull/6582>`__: MAINT:
linalg: make handling of infinite eigenvalues in `ordqz`...
- - `#6585 <https://github.com/scipy/scipy/pull/6585>`__: DOC:
interpolate: correct seealso links to ndimage
- - `#6588 <https://github.com/scipy/scipy/pull/6588>`__: Update
docstring of scipy.spatial.distance_matrix
- - `#6592 <https://github.com/scipy/scipy/pull/6592>`__: DOC: Replace
'first' by 'smallest' in mode
- - `#6593 <https://github.com/scipy/scipy/pull/6593>`__: MAINT:
remove scipy.weave submodule
- - `#6594 <https://github.com/scipy/scipy/pull/6594>`__: DOC:
distance.squareform: fix html docs, add note about dtype...
- - `#6598 <https://github.com/scipy/scipy/pull/6598>`__: [DOC] Fix
incorrect error message in medfilt2d
- - `#6599 <https://github.com/scipy/scipy/pull/6599>`__: MAINT:
linalg: turn a `solve_discrete_are` test back on
- - `#6600 <https://github.com/scipy/scipy/pull/6600>`__: DOC: Add SOS
goals to roadmap
- - `#6601 <https://github.com/scipy/scipy/pull/6601>`__: DEP: Raise
minimum numpy version to 1.8.2
- - `#6605 <https://github.com/scipy/scipy/pull/6605>`__: MAINT: 'new'
module is deprecated, don't use it
- - `#6607 <https://github.com/scipy/scipy/pull/6607>`__: DOC: add
note on change in wheel dependency on numpy and pip...
- - `#6609 <https://github.com/scipy/scipy/pull/6609>`__: Fixes #6602
- Typo in docs
- - `#6616 <https://github.com/scipy/scipy/pull/6616>`__: ENH:
generalization of continuous and discrete Riccati solvers...
- - `#6621 <https://github.com/scipy/scipy/pull/6621>`__: DOC: improve
cluster.hierarchy docstrings.
- - `#6623 <https://github.com/scipy/scipy/pull/6623>`__: CS matrix
prune method should copy data from large unpruned arrays
- - `#6625 <https://github.com/scipy/scipy/pull/6625>`__: DOC:
special: complete documentation of `eval_*` functions
- - `#6626 <https://github.com/scipy/scipy/pull/6626>`__: TST:
special: silence some deprecation warnings
- - `#6631 <https://github.com/scipy/scipy/pull/6631>`__: fix
parameter name doc for discrete distributions
- - `#6632 <https://github.com/scipy/scipy/pull/6632>`__: MAINT:
stats: change some instances of `special` to `sc`
- - `#6633 <https://github.com/scipy/scipy/pull/6633>`__: MAINT:
refguide: py2k long integers are equal to py3k integers
- - `#6638 <https://github.com/scipy/scipy/pull/6638>`__: MAINT:
change type declaration in cluster.linkage, prevent overflow
- - `#6640 <https://github.com/scipy/scipy/pull/6640>`__: BUG: fix
issue with duplicate values used in cluster.vq.kmeans
- - `#6641 <https://github.com/scipy/scipy/pull/6641>`__: BUG: fix
corner case in cluster.vq.kmeans for large thresholds
- - `#6643 <https://github.com/scipy/scipy/pull/6643>`__: MAINT: clean
up truncation modes of dendrogram
- - `#6645 <https://github.com/scipy/scipy/pull/6645>`__: MAINT:
special: rename `*_roots` functions
- - `#6646 <https://github.com/scipy/scipy/pull/6646>`__: MAINT: clean
up mpmath imports
- - `#6647 <https://github.com/scipy/scipy/pull/6647>`__: DOC: add
sqrt to Mahalanobis description for pdist
- - `#6648 <https://github.com/scipy/scipy/pull/6648>`__: DOC:
special: add a section on `cython_special` to the tutorial
- - `#6649 <https://github.com/scipy/scipy/pull/6649>`__: ENH: Added
scipy.spatial.distance.directed_hausdorff
- - `#6650 <https://github.com/scipy/scipy/pull/6650>`__: DOC: add
Sphinx roles for DOI and arXiv links
- - `#6651 <https://github.com/scipy/scipy/pull/6651>`__: BUG: mstats:
make sure mode(..., None) does not modify its input
- - `#6652 <https://github.com/scipy/scipy/pull/6652>`__: DOC:
special: add section to tutorial on functions not in special
- - `#6653 <https://github.com/scipy/scipy/pull/6653>`__: ENH:
special: add the Wright Omega function
- - `#6656 <https://github.com/scipy/scipy/pull/6656>`__: ENH: don't
coerce input to double with custom metric in cdist...
- - `#6658 <https://github.com/scipy/scipy/pull/6658>`__:
Faster/shorter code for computation of discordances
- - `#6659 <https://github.com/scipy/scipy/pull/6659>`__: DOC:
special: make __init__ summaries and html summaries match
- - `#6661 <https://github.com/scipy/scipy/pull/6661>`__: general.rst:
Fix a typo
- - `#6664 <https://github.com/scipy/scipy/pull/6664>`__: TST:
Spectral functions' window correction factor
- - `#6665 <https://github.com/scipy/scipy/pull/6665>`__: [DOC]
Conditions on v in RectSphereBivariateSpline
- - `#6668 <https://github.com/scipy/scipy/pull/6668>`__: DOC: Mention
negative masses for center of mass
- - `#6675 <https://github.com/scipy/scipy/pull/6675>`__: MAINT:
special: remove outdated README
- - `#6677 <https://github.com/scipy/scipy/pull/6677>`__: BUG: Fixes
computation of p-values.
- - `#6679 <https://github.com/scipy/scipy/pull/6679>`__: BUG:
optimize: return correct Jacobian for method 'SLSQP' in...
- - `#6680 <https://github.com/scipy/scipy/pull/6680>`__: ENH: Add
structural rank to sparse.csgraph
- - `#6686 <https://github.com/scipy/scipy/pull/6686>`__: TST: Added
Airspeed Velocity benchmarks for SphericalVoronoi
- - `#6687 <https://github.com/scipy/scipy/pull/6687>`__: DOC: add
section "deciding on new features" to developer guide.
- - `#6691 <https://github.com/scipy/scipy/pull/6691>`__: ENH: Clearer
error when fmin_slsqp obj doesn't return scalar
- - `#6702 <https://github.com/scipy/scipy/pull/6702>`__: TST: Added
airspeed velocity benchmarks for scipy.spatial.distance.cdist
- - `#6707 <https://github.com/scipy/scipy/pull/6707>`__: TST:
interpolate: test fitpack wrappers, not _impl
- - `#6709 <https://github.com/scipy/scipy/pull/6709>`__: TST: fix a
number of test failures on 32-bit systems
- - `#6711 <https://github.com/scipy/scipy/pull/6711>`__: MAINT: move
function definitions from __fitpack.h to _fitpackmodule.c
- - `#6712 <https://github.com/scipy/scipy/pull/6712>`__: MAINT: clean
up wishlist in stats.morestats, and copyright statement.
- - `#6715 <https://github.com/scipy/scipy/pull/6715>`__: DOC: update
the release notes with BSpline et al.
- - `#6716 <https://github.com/scipy/scipy/pull/6716>`__: MAINT:
scipy.io.wavfile: No infinite loop when trying to read...
- - `#6717 <https://github.com/scipy/scipy/pull/6717>`__: some style cleanup
- - `#6723 <https://github.com/scipy/scipy/pull/6723>`__: BUG:
special: cast to float before in-place multiplication in...
- - `#6726 <https://github.com/scipy/scipy/pull/6726>`__: address
performance regressions in interp1d
- - `#6728 <https://github.com/scipy/scipy/pull/6728>`__: DOC: made
code examples in `integrate` tutorial copy-pasteable
- - `#6731 <https://github.com/scipy/scipy/pull/6731>`__: DOC:
scipy.optimize: Added an example for wrapping complex-valued...
- - `#6732 <https://github.com/scipy/scipy/pull/6732>`__: MAINT:
cython_special: remove `errprint`
- - `#6733 <https://github.com/scipy/scipy/pull/6733>`__: MAINT:
special: fix some pyflakes warnings
- - `#6734 <https://github.com/scipy/scipy/pull/6734>`__: DOC:
sparse.linalg: fixed matrix description in `bicgstab` doc
- - `#6737 <https://github.com/scipy/scipy/pull/6737>`__: BLD: update
`cythonize.py` to detect changes in pxi files
- - `#6740 <https://github.com/scipy/scipy/pull/6740>`__: DOC:
special: some small fixes to docstrings
- - `#6741 <https://github.com/scipy/scipy/pull/6741>`__: MAINT:
remove dead code in interpolate.py
- - `#6742 <https://github.com/scipy/scipy/pull/6742>`__: BUG: fix
``linalg.block_diag`` to support zero-sized matrices.
- - `#6744 <https://github.com/scipy/scipy/pull/6744>`__: ENH:
interpolate: make PPoly.from_spline accept BSpline objects
- - `#6746 <https://github.com/scipy/scipy/pull/6746>`__: DOC:
special: clarify use of Condon-Shortley phase in `sph_harm`/`lpmv`
- - `#6750 <https://github.com/scipy/scipy/pull/6750>`__: ENH: sparse:
avoid densification on broadcasted elem-wise mult
- - `#6751 <https://github.com/scipy/scipy/pull/6751>`__: sinm doc
explained cosm
- - `#6753 <https://github.com/scipy/scipy/pull/6753>`__: ENH:
special: allow for more fine-tuned error handling
- - `#6759 <https://github.com/scipy/scipy/pull/6759>`__: Move
logsumexp and pade from scipy.misc to scipy.special and...
- - `#6761 <https://github.com/scipy/scipy/pull/6761>`__: ENH: argmax
and argmin methods for sparse matrices
- - `#6762 <https://github.com/scipy/scipy/pull/6762>`__: DOC: Improve
docstrings of sparse matrices
- - `#6763 <https://github.com/scipy/scipy/pull/6763>`__: ENH: Weighted tau
- - `#6768 <https://github.com/scipy/scipy/pull/6768>`__: ENH:
cythonized spherical Voronoi region polygon vertex sorting
- - `#6770 <https://github.com/scipy/scipy/pull/6770>`__: Correction
of Delaunay class' documentation
- - `#6775 <https://github.com/scipy/scipy/pull/6775>`__: ENH:
Integrating LAPACK "expert" routines with conditioning warnings...
- - `#6776 <https://github.com/scipy/scipy/pull/6776>`__: MAINT:
Removing the trivial f2py warnings
- - `#6777 <https://github.com/scipy/scipy/pull/6777>`__: DOC: Update
rv_continuous.fit doc.
- - `#6778 <https://github.com/scipy/scipy/pull/6778>`__: MAINT:
cluster.hierarchy: Improved wording of error msgs
- - `#6786 <https://github.com/scipy/scipy/pull/6786>`__: BLD:
increase minimum Cython version to 0.23.4
- - `#6787 <https://github.com/scipy/scipy/pull/6787>`__: DOC: expand
on ``linalg.block_diag`` changes in 0.19.0 release...
- - `#6789 <https://github.com/scipy/scipy/pull/6789>`__: ENH: Add
further documentation for norm.fit
- - `#6790 <https://github.com/scipy/scipy/pull/6790>`__: MAINT: Fix a
potential problem in nn_chain linkage algorithm
- - `#6791 <https://github.com/scipy/scipy/pull/6791>`__: DOC: Add
examples to scipy.ndimage.fourier
- - `#6792 <https://github.com/scipy/scipy/pull/6792>`__: DOC: fix
some numpydoc / Sphinx issues.
- - `#6793 <https://github.com/scipy/scipy/pull/6793>`__: MAINT: fix
circular import after moving functions out of misc
- - `#6796 <https://github.com/scipy/scipy/pull/6796>`__: TST: test
importing each submodule. Regression test for gh-6793.
- - `#6799 <https://github.com/scipy/scipy/pull/6799>`__: ENH: stats:
Argus distribution
- - `#6801 <https://github.com/scipy/scipy/pull/6801>`__: ENH: stats:
Histogram distribution
- - `#6803 <https://github.com/scipy/scipy/pull/6803>`__: TST: make
sure tests for ``_build_utils`` are run.
- - `#6804 <https://github.com/scipy/scipy/pull/6804>`__: MAINT: more
fixes in `loggamma`
- - `#6806 <https://github.com/scipy/scipy/pull/6806>`__: ENH: Faster
linkage for 'centroid' and 'median' methods
- - `#6810 <https://github.com/scipy/scipy/pull/6810>`__: ENH: speed
up upfirdn and resample_poly for n-dimensional arrays
- - `#6812 <https://github.com/scipy/scipy/pull/6812>`__: TST: Added
ConvexHull asv benchmark code
- - `#6814 <https://github.com/scipy/scipy/pull/6814>`__: ENH:
Different extrapolation modes for different dimensions in...
- - `#6826 <https://github.com/scipy/scipy/pull/6826>`__: Signal
spectral window default fix
- - `#6828 <https://github.com/scipy/scipy/pull/6828>`__: BUG:
SphericalVoronoi Space Complexity (Fixes #6811)
- - `#6830 <https://github.com/scipy/scipy/pull/6830>`__: RealData
docstring correction
- - `#6834 <https://github.com/scipy/scipy/pull/6834>`__: DOC: Added
reference for skewtest function. See #6829
- - `#6836 <https://github.com/scipy/scipy/pull/6836>`__: DOC: Added
mode='mirror' in the docstring for the functions accepting...
- - `#6838 <https://github.com/scipy/scipy/pull/6838>`__: MAINT:
sparse: start removing old BSR methods
- - `#6844 <https://github.com/scipy/scipy/pull/6844>`__: handle
incompatible dimensions when input is not an ndarray in...
- - `#6847 <https://github.com/scipy/scipy/pull/6847>`__: Added
maxiter to golden search.
- - `#6850 <https://github.com/scipy/scipy/pull/6850>`__: BUG: added
check for optional param scipy.stats.spearmanr
- - `#6858 <https://github.com/scipy/scipy/pull/6858>`__: MAINT:
Removing redundant tests
- - `#6861 <https://github.com/scipy/scipy/pull/6861>`__: DEP: Fix
escape sequences deprecated in Python 3.6.
- - `#6862 <https://github.com/scipy/scipy/pull/6862>`__: DOC: dx
should be float, not int
- - `#6863 <https://github.com/scipy/scipy/pull/6863>`__: updated
documentation curve_fit
- - `#6866 <https://github.com/scipy/scipy/pull/6866>`__: DOC : added
some documentation to j1 referring to spherical_jn
- - `#6867 <https://github.com/scipy/scipy/pull/6867>`__: DOC: cdist
move long examples list into Notes section
- - `#6868 <https://github.com/scipy/scipy/pull/6868>`__: BUG: Make
stats.mode return a ModeResult namedtuple on empty...
- - `#6871 <https://github.com/scipy/scipy/pull/6871>`__: Corrected
documentation.
- - `#6874 <https://github.com/scipy/scipy/pull/6874>`__: ENH:
gaussian_kde.logpdf based on logsumexp
- - `#6877 <https://github.com/scipy/scipy/pull/6877>`__: BUG:
ndimage: guard against footprints of all zeros
- - `#6881 <https://github.com/scipy/scipy/pull/6881>`__: python 3.6
- - `#6885 <https://github.com/scipy/scipy/pull/6885>`__: Vectorized
integrate.fixed_quad
- - `#6886 <https://github.com/scipy/scipy/pull/6886>`__: fixed typo
- - `#6891 <https://github.com/scipy/scipy/pull/6891>`__: TST: fix
failures for linalg.dare/care due to tightened test...
- - `#6892 <https://github.com/scipy/scipy/pull/6892>`__: DOC: fix a
bunch of Sphinx errors.
- - `#6894 <https://github.com/scipy/scipy/pull/6894>`__: TST: Added
asv benchmarks for scipy.spatial.Voronoi
- - `#6908 <https://github.com/scipy/scipy/pull/6908>`__: BUG: Fix
return dtype for complex input in spsolve
- - `#6909 <https://github.com/scipy/scipy/pull/6909>`__: ENH:
fftpack: use float32 routines for float16 inputs.
- - `#6911 <https://github.com/scipy/scipy/pull/6911>`__: added
min/max support to binned_statistic
- - `#6913 <https://github.com/scipy/scipy/pull/6913>`__: Fix 6875:
SLSQP raise ValueError for all invalid bounds.
- - `#6914 <https://github.com/scipy/scipy/pull/6914>`__: DOCS: GH6903
updating docs of Spatial.distance.pdist
- - `#6916 <https://github.com/scipy/scipy/pull/6916>`__: MAINT: fix
some issues for 32-bit Python
- - `#6924 <https://github.com/scipy/scipy/pull/6924>`__: BLD: update
Bento build for scipy.LowLevelCallable
- - `#6932 <https://github.com/scipy/scipy/pull/6932>`__: ENH: Use
OrderedDict in io.netcdf. Closes gh-5537
- - `#6933 <https://github.com/scipy/scipy/pull/6933>`__: BUG: fix
LowLevelCallable issue on 32-bit Python.
- - `#6936 <https://github.com/scipy/scipy/pull/6936>`__: BUG: sparse:
handle size-1 2D indexes correctly
- - `#6938 <https://github.com/scipy/scipy/pull/6938>`__: TST: fix
test failures in special on 32-bit Python.
- - `#6939 <https://github.com/scipy/scipy/pull/6939>`__: Added
attributes list to cKDTree docstring
- - `#6940 <https://github.com/scipy/scipy/pull/6940>`__: improve
efficiency of dok_matrix.tocoo
- - `#6942 <https://github.com/scipy/scipy/pull/6942>`__: DOC: add
link to liac-arff package in the io.arff docstring.
- - `#6943 <https://github.com/scipy/scipy/pull/6943>`__: MAINT:
Docstring fixes and an additional test for linalg.solve
- - `#6944 <https://github.com/scipy/scipy/pull/6944>`__: DOC: Add
example of odeint with a banded Jacobian to the integrate...
- - `#6946 <https://github.com/scipy/scipy/pull/6946>`__: ENH:
hypergeom.logpmf in terms of betaln
- - `#6947 <https://github.com/scipy/scipy/pull/6947>`__: TST: speedup
distance tests
- - `#6948 <https://github.com/scipy/scipy/pull/6948>`__: DEP:
Deprecate the keyword "debug" from linalg.solve
- - `#6950 <https://github.com/scipy/scipy/pull/6950>`__: BUG:
Correctly treat large integers in MMIO (fixes #6397)
- - `#6952 <https://github.com/scipy/scipy/pull/6952>`__: ENH: Minor
user-friendliness cleanup in LowLevelCallable
- - `#6956 <https://github.com/scipy/scipy/pull/6956>`__: DOC: improve
description of 'output' keyword for convolve
- - `#6957 <https://github.com/scipy/scipy/pull/6957>`__: ENH more
informative error in sparse.bmat
- - `#6962 <https://github.com/scipy/scipy/pull/6962>`__: Shebang fixes
- - `#6964 <https://github.com/scipy/scipy/pull/6964>`__: DOC: note
argmin/argmax addition
- - `#6965 <https://github.com/scipy/scipy/pull/6965>`__: BUG: Fix
issues passing error tolerances in dblquad and tplquad.
- - `#6971 <https://github.com/scipy/scipy/pull/6971>`__: fix the
docstring of signaltools.correlate
- - `#6973 <https://github.com/scipy/scipy/pull/6973>`__: Silence
expected numpy warnings in scipy.ndimage.interpolation.zoom()
- - `#6975 <https://github.com/scipy/scipy/pull/6975>`__: BUG:
special: fix regex in `generate_ufuncs.py`
- - `#6976 <https://github.com/scipy/scipy/pull/6976>`__: Update
docstring for griddata
- - `#6978 <https://github.com/scipy/scipy/pull/6978>`__: Avoid
division by zero in zoom factor calculation
- - `#6979 <https://github.com/scipy/scipy/pull/6979>`__: BUG: ARE
solvers did not check the generalized case carefully
- - `#6985 <https://github.com/scipy/scipy/pull/6985>`__: ENH: sparse:
add scipy.sparse.linalg.spsolve_triangular
- - `#6994 <https://github.com/scipy/scipy/pull/6994>`__: MAINT:
spatial: updates to plotting utils
- - `#6995 <https://github.com/scipy/scipy/pull/6995>`__: DOC: Bad
documentation of k in sparse.linalg.eigs See #6990
- - `#6997 <https://github.com/scipy/scipy/pull/6997>`__: TST: Changed
the test with a less singular example
- - `#7000 <https://github.com/scipy/scipy/pull/7000>`__: DOC: clarify
interp1d 'zero' argument
- - `#7007 <https://github.com/scipy/scipy/pull/7007>`__: BUG: Fix
division by zero in linregress() for 2 data points
- - `#7009 <https://github.com/scipy/scipy/pull/7009>`__: BUG: Fix
problem in passing drop_rule to scipy.sparse.linalg.spilu
- - `#7012 <https://github.com/scipy/scipy/pull/7012>`__: speed
improvment in _distn_infrastructure.py
- - `#7014 <https://github.com/scipy/scipy/pull/7014>`__: Fix Typo:
add a single quotation mark to fix a slight typo
- - `#7021 <https://github.com/scipy/scipy/pull/7021>`__: MAINT:
stats: use machine constants from np.finfo, not machar
- - `#7026 <https://github.com/scipy/scipy/pull/7026>`__: MAINT: update .mailmap
- - `#7032 <https://github.com/scipy/scipy/pull/7032>`__: Fix layout
of rv_histogram docs
- - `#7035 <https://github.com/scipy/scipy/pull/7035>`__: DOC: update
0.19.0 release notes
- - `#7036 <https://github.com/scipy/scipy/pull/7036>`__: ENH: Add
more boundary options to signal.stft
- - `#7040 <https://github.com/scipy/scipy/pull/7040>`__: TST: stats:
skip too slow tests
- - `#7042 <https://github.com/scipy/scipy/pull/7042>`__: MAINT:
sparse: speed up setdiag tests
- - `#7043 <https://github.com/scipy/scipy/pull/7043>`__: MAINT:
refactory and code cleaning Xdist
- - `#7053 <https://github.com/scipy/scipy/pull/7053>`__: Fix msvc 9
and 10 compile errors
- - `#7060 <https://github.com/scipy/scipy/pull/7060>`__: DOC: updated
release notes with #7043 and #6656
- - `#7062 <https://github.com/scipy/scipy/pull/7062>`__: MAINT:
Change defaut STFT boundary kwarg to "zeros"
- - `#7064 <https://github.com/scipy/scipy/pull/7064>`__: Fix
ValueError: path is on mount 'X:', start on mount 'D:' on...
- - `#7067 <https://github.com/scipy/scipy/pull/7067>`__: TST: Fix
PermissionError: [Errno 13] Permission denied on Windows
- - `#7068 <https://github.com/scipy/scipy/pull/7068>`__: TST: Fix
UnboundLocalError: local variable 'data' referenced...
- - `#7069 <https://github.com/scipy/scipy/pull/7069>`__: Fix
OverflowError: Python int too large to convert to C long...
- - `#7071 <https://github.com/scipy/scipy/pull/7071>`__: TST: silence
RuntimeWarning for nan test of stats.spearmanr
- - `#7072 <https://github.com/scipy/scipy/pull/7072>`__: Fix
OverflowError: Python int too large to convert to C long...
- - `#7084 <https://github.com/scipy/scipy/pull/7084>`__: TST: linalg:
bump tolerance in test_falker
- - `#7095 <https://github.com/scipy/scipy/pull/7095>`__: TST: linalg:
bump more tolerances in test_falker
- - `#7101 <https://github.com/scipy/scipy/pull/7101>`__: TST: Relax
solve_continuous_are test case 2 and 12
- - `#7106 <https://github.com/scipy/scipy/pull/7106>`__: BUG: stop
cdist "correlation" modifying input
- - `#7116 <https://github.com/scipy/scipy/pull/7116>`__: Backports to 0.19.0rc2
Checksums
=========
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~~~~~~
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