Update dependency scipy to v0.19.1 #156
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This PR contains the following updates:
==0.17.1->==0.19.1Release Notes
scipy/scipy
v0.19.1Compare Source
SciPy 0.19.1 Release Notes
SciPy 0.19.1 is a bug-fix release with no new features compared to 0.19.0.
The most important change is a fix for a severe memory leak in
integrate.quad.Authors
A total of 9 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 0.19.1
linalg.matrix_balancegives wrong transformation matrixscipy.interpolate._bspl.evaluate_splinegets wrong typePull requests for 0.19.1
sparse.load_npz,save_npzv0.19.0Compare Source
SciPy 0.19.0 Release Notes
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:
scipy.LowLevelCallable.the
scipy.specialmodule, viacimport scipy.special.cython_special.New features
Foreign function interface improvements
scipy.LowLevelCallableprovides a new unified interface for wrappinglow-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.linalgimprovementsThe function
scipy.linalg.solveobtained two more keywordsassume_aandtransposed. 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_poskeyword is kept for backwards compatibility reasons however itis identical to using
assume_a='pos'. Moreover, thedebugkeyword,which had no function but only printing the
overwrite_<a, b>values, isdeprecated.
The function
scipy.linalg.matrix_balancewas added to perform the so-calledmatrix 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_areandscipy.linalg.solve_discrete_arehave numerically more stable algorithms.These functions can also solve generalized algebraic matrix Riccati equations.
Moreover, both gained a
balancedkeyword to turn balancing on and off.scipy.spatialimprovementsscipy.spatial.SphericalVoronoi.sort_vertices_of_regionshas been re-written inCython to improve performance.
scipy.spatial.SphericalVoronoican handle > 200 k points (at least 10 million)and has improved performance.
The function
scipy.spatial.distance.directed_hausdorffwasadded to calculate the directed Hausdorff distance.
count_neighborsmethod ofscipy.spatial.cKDTreegained an ability toperform weighted pair counting via the new keywords
weightsandcumulative. Seegh-5647 <https://github.com/scipy/scipy/pull/5647>_ fordetails.
scipy.spatial.distance.pdistandscipy.spatial.distance.cdistnow supportnon-double custom metrics.
scipy.ndimageimprovementsThe callback function C API supports PyCapsules in Python 2.7
Multidimensional filters now allow having different extrapolation modes for
different axes.
scipy.optimizeimprovementsThe
scipy.optimize.basinhoppingglobal minimizer obtained a new keyword,seed, which can be used to seed the random number generator and obtainrepeatable minimizations.
The keyword
sigmainscipy.optimize.curve_fitwas overloaded to also acceptthe covariance matrix of errors in the data.
scipy.signalimprovementsThe function
scipy.signal.correlateandscipy.signal.convolvehave a newoptional parameter
method. The default value ofautoestimates the fastestof 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_methodwhich may be appropriate if convolutions orcorrelations 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.stftandscipy.signal.istft. Thisimplementation also fixes the previously incorrect ouput of
scipy.signal.spectrogramwhen complex output data were requested.The function
scipy.signal.sosfreqzwas added to compute the frequencyresponse from second-order sections.
The function
scipy.signal.unit_impulsewas added to convenientlygenerate an impulse function.
The function
scipy.signal.iirnotchwas added to design second-orderIIR notch filters that can be used to remove a frequency component from
a signal. The dual function
scipy.signal.iirpeakwas added tocompute the coefficients of a second-order IIR peak (resonant) filter.
The function
scipy.signal.minimum_phasewas added to convert linear-phaseFIR filters to minimum phase.
The functions
scipy.signal.upfirdnandscipy.signal.resample_polyare nowsubstantially 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.fftpackimprovementsFast Fourier transform routines now accept
np.float16inputs and upcastthem to
np.float32. Previously, they would raise an error.scipy.clusterimprovementsMethods
"centroid"and"median"ofscipy.cluster.hierarchy.linkagehave been significantly sped up. Long-standing issues with using
linkageonlarge input data (over 16 GB) have been resolved.
scipy.sparseimprovementsThe functions
scipy.sparse.save_npzandscipy.sparse.load_npzwere added,providing simple serialization for some sparse formats.
The
prunemethod of classesbsr_matrix,csc_matrix, andcsr_matrixwas updated to reallocate backing arrays under certain conditions, reducing
memory usage.
The methods
argminandargmaxwere added to classescoo_matrix,csc_matrix,csr_matrix, andbsr_matrix.New function
scipy.sparse.csgraph.structural_rankcomputes the structuralrank of a graph with a given sparsity pattern.
New function
scipy.sparse.linalg.spsolve_triangularsolves a sparse linearsystem with a triangular left hand side matrix.
scipy.specialimprovementsScalar, typed versions of universal functions from
scipy.specialare availablein the Cython space via
cimportfrom the new modulescipy.special.cython_special. These scalar functions can be expected to besignificantly faster then the universal functions for scalar arguments. See
the
scipy.specialtutorial for details.Better control over special-function errors is offered by the
functions
scipy.special.geterrandscipy.special.seterrand thecontext 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_rootshas been renamedscipy.special.roots_jacobifor consistency with the relatedfunctions
scipy.special.jacobiandscipy.special.eval_jacobi. Topreserve back-compatibility the old names have been left as aliases.
Wright Omega function is implemented as
scipy.special.wrightomega.scipy.statsimprovementsThe function
scipy.stats.weightedtauwas added. It provides a weightedversion of Kendall's tau.
New class
scipy.stats.multinomialimplements the multinomial distribution.New class
scipy.stats.rv_histogramconstructs a continuous univariatedistribution with a piecewise linear CDF from a binned data sample.
New class
scipy.stats.argusimplements the Argus distribution.scipy.interpolateimprovementsNew class
scipy.interpolate.BSplinerepresents splines.BSplineobjectscontain knots and coefficients and can evaluate the spline. The format is
consistent with FITPACK, so that one can do, for example::
spl*functions,scipy.interpolate.splev,scipy.interpolate.splint,scipy.interpolate.splderandscipy.interpolate.splantider, accept bothBSplineobjects and(t, c, k)tuples for backwards compatibility.For multidimensional splines,
c.ndim > 1,BSplineobjects are consistentwith piecewise polynomials,
scipy.interpolate.PPoly. This means thatBSplineobjects are not immediately consistent withscipy.interpolate.splprep, and one cannot do>>> BSpline(*splprep([x, y])[0]). Consult thescipy.interpolatetest suitefor examples of the precise equivalence.
In new code, prefer using
scipy.interpolate.BSplineobjects instead ofmanipulating
(t, c, k)tuples directly.New function
scipy.interpolate.make_interp_splineconstructs an interpolatingspline given data points and boundary conditions.
New function
scipy.interpolate.make_lsq_splineconstructs a least-squaresspline approximation given data points.
scipy.integrateimprovementsNow
scipy.integrate.fixed_quadsupports vector-valued functions.Deprecated features
scipy.interpolate.splmake,scipy.interpolate.splevalandscipy.interpolate.splineare deprecated. The format used bysplmake/splevalwas inconsistent with
splrep/splevwhich was confusing to users.scipy.special.errprintis deprecated. Improved functionality isavailable in
scipy.special.seterr.calling
scipy.spatial.distance.pdistorscipy.spatial.distance.cdistwitharguments not needed by the chosen metric is deprecated. Also, metrics
"old_cosine"and"old_cos"are deprecated.Backwards incompatible changes
The deprecated
scipy.weavesubmodule was removed.scipy.spatial.distance.squareformnow returns arrays of the same dtype asthe input, instead of always float64.
scipy.special.errprintnow returns a boolean.The function
scipy.signal.find_peaks_cwtnow returns an array instead ofa list.
scipy.stats.kendalltaunow computes the correct p-value in case theinput contains ties. The p-value is also identical to that computed by
scipy.stats.mstats.kendalltauand by R. If the input does notcontain ties there is no change w.r.t. the previous implementation.
The function
scipy.linalg.block_diagwill 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
numpyon 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-neededforpip >= 8.2; that behavior willbecome the default in the next major version of
pip).Numerical values returned by
scipy.interpolate.interp1dwithkind="cubic"and
"quadratic"may change relative to previous scipy versions. If yourcode depended on specific numeric values (i.e., on implementation
details of the interpolators), you may want to double-check your results.
Authors
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.
v0.18.1Compare Source
SciPy 0.18.1 Release Notes
SciPy 0.18.1 is a bug-fix release with no new features compared to 0.18.0.
Authors
A total of 9 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 0.18.1
#​6357 <https://github.com/scipy/scipy/issues/6357>__: scipy 0.17.1 piecewise cubic hermite interpolation does not return...#​6420 <https://github.com/scipy/scipy/issues/6420>__: circmean() changed behaviour from 0.17 to 0.18#​6421 <https://github.com/scipy/scipy/issues/6421>__: scipy.linalg.solve_banded overwrites input 'b' when the inversion...#​6425 <https://github.com/scipy/scipy/issues/6425>__: cKDTree INF bug#​6435 <https://github.com/scipy/scipy/issues/6435>__: scipy.stats.ks_2samp returns different values on different computers#​6458 <https://github.com/scipy/scipy/issues/6458>__: Error in scipy.integrate.dblquad when using variable integration...Pull requests for 0.18.1
#​6405 <https://github.com/scipy/scipy/pull/6405>__: BUG: sparse: fix elementwise divide for CSR/CSC#​6431 <https://github.com/scipy/scipy/pull/6431>__: BUG: result for insufficient neighbours from cKDTree is wrong.#​6432 <https://github.com/scipy/scipy/pull/6432>__: BUG Issue #6421: scipy.linalg.solve_banded overwrites input 'b'...#​6455 <https://github.com/scipy/scipy/pull/6455>__: DOC: add links to release notes#​6462 <https://github.com/scipy/scipy/pull/6462>__: BUG: interpolate: fix .roots method of PchipInterpolator#​6492 <https://github.com/scipy/scipy/pull/6492>__: BUG: Fix regression in dblquad: #6458#​6543 <https://github.com/scipy/scipy/pull/6543>__: fix the regression in circmean#​6545 <https://github.com/scipy/scipy/pull/6545>__: Revert gh-5938, restore ks_2samp#​6557 <https://github.com/scipy/scipy/pull/6557>__: Backports for 0.18.1v0.18.0Compare Source
SciPy 0.18.0 Release Notes
SciPy 0.18.0 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. 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 3.4-3.5 and NumPy 1.7.1 or greater.
Highlights of this release include:
scipy.optimize.solve_bvp.CubicSpline, for cubic spline interpolation of data.scipy.interpolate.NdPPoly.scipy.spatial.SphericalVoronoi.scipy.signal.dlti.New features
scipy.integrateimprovementsA solver of two-point boundary value problems for ODE systems has been
implemented in
scipy.integrate.solve_bvp. The solver allows for non-separatedboundary conditions, unknown parameters and certain singular terms. It finds
a C1 continious solution using a fourth-order collocation algorithm.
scipy.interpolateimprovementsCubic spline interpolation is now available via
scipy.interpolate.CubicSpline.This class represents a piecewise cubic polynomial passing through given points
and C2 continuous. It is represented in the standard polynomial basis on each
segment.
A representation of n-dimensional tensor product piecewise polynomials is
available as the
scipy.interpolate.NdPPolyclass.Univariate piecewise polynomial classes,
PPolyandBpoly, can now beevaluated on periodic domains. Use
extrapolate="periodic"keywordargument for this.
scipy.fftpackimprovementsscipy.fftpack.next_fast_lenfunction computes the next "regular" number forFFTPACK. Padding the input to this length can give significant performance
increase for
scipy.fftpack.fft.scipy.signalimprovementsResampling using polyphase filtering has been implemented in the function
scipy.signal.resample_poly. This method upsamples a signal, applies azero-phase low-pass FIR filter, and downsamples using
scipy.signal.upfirdn(which is also new in 0.18.0). This method can be faster than FFT-based
filtering provided by
scipy.signal.resamplefor some signals.scipy.signal.firls, which constructs FIR filters using least-squares errorminimization, was added.
scipy.signal.sosfiltfilt, which does forward-backward filtering likescipy.signal.filtfiltbut for second-order sections, was added.Discrete-time linear systems
scipy.stats.special_ortho_groupandscipy.stats.ortho_groupprovidegenerators of random matrices in the SO(N) and O(N) groups, respectively. They
generate matrices in the Haar distribution, the only uniform distribution on
these group manifolds.
scipy.stats.random_correlationprovides a generator for randomcorrelation matrices, given specified eigenvalues.
scipy.linalgimprovementsscipy.linalg.svdgained a new keyword argument,lapack_driver. Availabledrivers are
gesdd(default) andgesvd.scipy.linalg.lapack.ilaverreturns the version of the LAPACK library SciPylinks to.
scipy.spatialimprovementsBoolean distances,
scipy.spatial.pdist, have been sped up. Improvements varyby the function and the input size. In many cases, one can expect a speed-up
of x2--x10.
New class
scipy.spatial.SphericalVoronoiconstructs Voronoi diagrams on thesurface of a sphere. See pull request gh-5232 for details.
scipy.clusterimprovementsA new clustering algorithm, the nearest neighbor chain algorithm, has been
implemented for
scipy.cluster.hierarchy.linkage. As a result, one can expecta significant algorithmic improvement (:math:
O(N^2)instead of :math:O(N^3))for several linkage methods.
scipy.specialimprovementsThe new function
scipy.special.loggammacomputes the principal branch of thelogarithm of the Gamma function. For real input,
loggammais compatiblewith
scipy.special.gammaln. For complex input, it has more consistentbehavior in the complex plane and should be preferred over
gammaln.Vectorized forms of spherical Bessel functions have been implemented as
scipy.special.spherical_jn,scipy.special.spherical_kn,scipy.special.spherical_inandscipy.special.spherical_yn.They are recommended for use over
sph_*functions, which are now deprecated.Several special functions have been extended to the complex domain and/or
have seen domain/stability improvements. This includes
spence,digamma,log1pand several others.Deprecated features
The cross-class properties of
ltisystems have been deprecated. Thefollowing properties/setters will raise a
DeprecationWarning:Name - (accessing/setting raises warning) - (setting raises warning)
num,den,gain) - (zeros,poles)A,B,C,D,gain) - (zeros,poles)A,B,C,D,num,den) - ()Spherical Bessel functions,
sph_in,sph_jn,sph_kn,sph_yn,sph_jnynandsph_inknhave been deprecated in favor ofscipy.special.spherical_jnandspherical_kn,spherical_yn,spherical_in.The following functions in
scipy.constantsare deprecated:C2K,K2C,C2F,F2C,F2KandK2F. They are superceded by a new functionscipy.constants.convert_temperaturethat can perform all those conversionsplus to/from the Rankine temperature scale.
Backwards incompatible changes
scipy.optimizeThe convergence criterion for
optimize.bisect,optimize.brentq,optimize.brenth, andoptimize.riddernowworks the same as
numpy.allclose.scipy.ndimageThe offset in
ndimage.iterpolation.affine_transformis now consistently added after the matrix is applied,
independent of if the matrix is specified using a one-dimensional
or a two-dimensional array.
scipy.statsstats.ks_2sampused to return nonsensical values if the input wasnot real or contained nans. It now raises an exception for such inputs.
Several deprecated methods of
scipy.statsdistributions have been removed:est_loc_scale,vecfunc,veccdfandvec_generic_moment.Deprecated functions
nanmean,nanstdandnanmedianhave been removedfrom
scipy.stats. These functions were deprecated in scipy 0.15.0 in favorof their
numpyequivalents.A bug in the
rvs()method of the distributions inscipy.statshasbeen fixed. When arguments to
rvs()were given that were shaped forbroadcasting, in many cases the returned random samples were not random.
A simple example of the problem is
stats.norm.rvs(loc=np.zeros(10)).Because of the bug, that call would return 10 identical values. The bug
only affected code that relied on the broadcasting of the shape, location
and scale parameters.
The
rvs()method also accepted some arguments that it should not have.There is a potential for backwards incompatibility in cases where
rvs()accepted arguments that are not, in fact, compatible with broadcasting.
An example is
The shape of the first argument is not compatible with the requested size,
but the function still returned an array with shape (2, 2). In scipy 0.18,
that call generates a
ValueError.scipy.ioscipy.io.netcdfmasking now gives precedence to the_FillValueattributeover the
missing_valueattribute, if both are given. Also, data are onlytreated as missing if they match one of these attributes exactly: values that
differ by roundoff from
_FillValueormissing_valueare no longertreated as missing values.
scipy.interpolatescipy.interpolate.PiecewisePolynomialclass has been removed. It has beendeprecated in scipy 0.14.0, and
scipy.interpolate.BPoly.from_derivativesservesas a drop-in replacement.
Other changes
Scipy now uses
setuptoolsfor its builds instead of plain distutils. Thisfixes usage of
install_requires='scipy'in thesetup.pyfiles ofprojects that depend on Scipy (see Numpy issue gh-6551 for details). It
potentially affects the way that build/install methods for Scipy itself behave
though. Please report any unexpected behavior on the Scipy issue tracker.
PR
#​6240 <https://github.com/scipy/scipy/pull/6240>__changes the interpretation of the
maxfunoption inL-BFGS-Bbased routinesin the
scipy.optimizemodule.An
L-BFGS-Bsearch consists of multiple iterations,with each iteration consisting of one or more function evaluations.
Whereas the old search strategy terminated immediately upon reaching
maxfunfunction evaluations, the new strategy allows the current iteration
to finish despite reaching
maxfun.The bundled copy of Qhull in the
scipy.spatialsubpackage has been upgraded toversion 2015.2.
The bundled copy of ARPACK in the
scipy.sparse.linalgsubpackage has beenupgraded to arpack-ng 3.3.0.
The bundled copy of SuperLU in the
scipy.sparsesubpackage has been upgradedto version 5.1.1.
Authors
A total of 99 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.
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