tylerjereddy released this
Aug 9, 2019
· 574 commits to master since this release
SciPy 1.3.1 is a bug-fix release with no new features compared to 1.3.0.
A total of 15 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.
tylerjereddy released this
Jun 7, 2019
· 1065 commits to master since this release
SciPy 1.2.2 is a bug-fix release with no new features compared to 1.2.1. Importantly, the SciPy 1.2.2 wheels are built with OpenBLAS 0.3.7.dev to alleviate issues with SkylakeX AVX512 kernels.
A total of 7 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.
tylerjereddy released this
May 17, 2019
· 112 commits to master since this release
SciPy 1.3.0 is the culmination of 5 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been some API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Before upgrading, we recommend that users check that their own code does not use deprecated SciPy functionality (to do so, run your code with python -Wd and check for DeprecationWarning s). Our development attention will now shift to bug-fix releases on the 1.3.x branch, and on adding new features on the master branch.
This release requires Python 3.5+ and NumPy 1.13.3 or greater.
For running on PyPy, PyPy3 6.0+ and NumPy 1.15.0 are required.
A new class CubicHermiteSpline is introduced. It is a piecewise-cubic interpolator which matches observed values and first derivatives. Existing cubic interpolators CubicSpline, PchipInterpolator and Akima1DInterpolator were made subclasses of CubicHermiteSpline.
For the Attribute-Relation File Format (ARFF) scipy.io.arff.loadarff now supports relational attributes.
scipy.io.mmread can now parse Matrix Market format files with empty lines.
Added wrappers for ?syconv routines, which convert a symmetric matrix given by a triangular matrix factorization into two matrices and vice versa.
scipy.linalg.clarkson_woodruff_transform now uses an algorithm that leverages sparsity. This may provide a 60-90 percent speedup for dense input matrices. Truly sparse input matrices should also benefit from the improved sketch algorithm, which now correctly runs in O(nnz(A)) time.
Added new functions to calculate symmetric Fiedler matrices and Fiedler companion matrices, named scipy.linalg.fiedler and scipy.linalg.fiedler_companion, respectively. These may be used for root finding.
Gaussian filter performances may improve by an order of magnitude in some cases, thanks to removal of a dependence on np.polynomial. This may impact scipy.ndimage.gaussian_filter for example.
The scipy.optimize.brute minimizer obtained a new keyword workers, which can be used to parallelize computation.
A Cython API for bounded scalar-function root-finders in scipy.optimize is available in a new module scipy.optimize.cython_optimize via cimport. This API may be used with nogil and prange to loop over an array of function arguments to solve for an array of roots more quickly than with pure Python.
'interior-point' is now the default method for linprog, and 'interior-point' now uses SuiteSparse for sparse problems when the required scikits (scikit-umfpack and scikit-sparse) are available. On benchmark problems (gh-10026), execution time reductions by factors of 2-3 were typical. Also, a new method='revised simplex' has been added. It is not as fast or robust as method='interior-point', but it is a faster, more robust, and equally accurate substitute for the legacy method='simplex'.
differential_evolution can now use a Bounds class to specify the bounds for the optimizing argument of a function.
scipy.optimize.dual_annealing performance improvements related to vectorisation of some internal code.
Two additional methods of discretization are now supported by scipy.signal.cont2discrete: impulse and foh.
scipy.signal.firls now uses faster solvers
scipy.signal.detrend now has a lower physical memory footprint in some cases, which may be leveraged using the new overwrite_data keyword argument
scipy.signal.firwin pass_zero argument now accepts new string arguments that allow specification of the desired filter type: 'bandpass', 'lowpass', 'highpass', and 'bandstop'
scipy.signal.sosfilt may have improved performance due to lower retention of the global interpreter lock (GIL) in algorithm
A new keyword was added to csgraph.dijsktra that allows users to query the shortest path to ANY of the passed in indices, as opposed to the shortest path to EVERY passed index.
scipy.sparse.linalg.lsmr performance has been improved by roughly 10 percent on large problems
Improved performance and reduced physical memory footprint of the algorithm used by scipy.sparse.linalg.lobpcg
CSR and CSC sparse matrix fancy indexing performance has been improved substantially
scipy.spatial.ConvexHull now has a good attribute that can be used alongsize the QGn Qhull options to determine which external facets of a convex hull are visible from an external query point.
scipy.spatial.cKDTree.query_ball_point has been modernized to use some newer Cython features, including GIL handling and exception translation. An issue with return_sorted=True and scalar queries was fixed, and a new mode named return_length was added. return_length only computes the length of the returned indices list instead of allocating the array every time.
scipy.spatial.transform.RotationSpline has been added to enable interpolation of rotations with continuous angular rates and acceleration
Added a new function to compute the Epps-Singleton test statistic, scipy.stats.epps_singleton_2samp, which can be applied to continuous and discrete distributions.
New functions scipy.stats.median_absolute_deviation and scipy.stats.gstd (geometric standard deviation) were added. The scipy.stats.combine_pvalues method now supports pearson, tippett and mudholkar_george pvalue combination methods.
The scipy.stats.ortho_group and scipy.stats.special_ortho_group rvs(dim) functions' algorithms were updated from a O(dim^4) implementation to a O(dim^3) which gives large speed improvements for dim>100.
A rewrite of scipy.stats.pearsonr to use a more robust algorithm, provide meaningful exceptions and warnings on potentially pathological input, and fix at least five separate reported issues in the original implementation.
Improved the precision of hypergeom.logcdf and hypergeom.logsf.
Added exact computation for Kolmogorov-Smirnov (KS) two-sample test, replacing the previously approximate computation for the two-sided test stats.ks_2samp. Also added a one-sided, two-sample KS test, and a keyword alternative to stats.ks_2samp.
Functions from scipy.interpolate (spleval, spline, splmake, and spltopp) and functions from scipy.misc (bytescale, fromimage, imfilter, imread, imresize, imrotate, imsave, imshow, toimage) have been removed. The former set has been deprecated since v0.19.0 and the latter has been deprecated since v1.0.0. Similarly, aliases from scipy.misc (comb, factorial, factorial2, factorialk, logsumexp, pade, info, source, who) which have been deprecated since v1.0.0 are removed. SciPy documentation for v1.1.0 <https://docs.scipy.org/doc/scipy-1.1.0/reference/misc.html>__ can be used to track the new import locations for the relocated functions.
SciPy documentation for v1.1.0 <https://docs.scipy.org/doc/scipy-1.1.0/reference/misc.html>
For pinv, pinv2, and pinvh, the default cutoff values are changed for consistency (see the docs for the actual values).
The default method for linprog is now 'interior-point'. The method's robustness and speed come at a cost: solutions may not be accurate to machine precision or correspond with a vertex of the polytope defined by the constraints. To revert to the original simplex method, include the argument method='simplex'.
Previously, ks_2samp(data1, data2) would run a two-sided test and return the approximated p-value. The new signature, ks_2samp(data1, data2, alternative="two-sided", method="auto"), still runs the two-sided test by default but returns the exact p-value for small samples and the approximated value for large samples. method="asymp" would be equivalent to the old version but auto is the better choice.
ks_2samp(data1, data2, alternative="two-sided", method="auto")
Our tutorial has been expanded with a new section on global optimizers
There has been a rework of the stats.distributions tutorials.
scipy.optimize now correctly sets the convergence flag of the result to CONVERR, a convergence error, for bounded scalar-function root-finders if the maximum iterations has been exceeded, disp is false, and full_output is true.
scipy.optimize.curve_fit no longer fails if xdata and ydata dtypes differ; they are both now automatically cast to float64.
scipy.ndimage functions including binary_erosion, binary_closing, and binary_dilation now require an integer value for the number of iterations, which alleviates a number of reported issues.
Fixed normal approximation in case zero_method == "pratt" in scipy.stats.wilcoxon.
zero_method == "pratt"
Fixes for incorrect probabilities, broadcasting issues and thread-safety related to stats distributions setting member variables inside _argcheck().
scipy.optimize.newton now correctly raises a RuntimeError, when default arguments are used, in the case that a derivative of value zero is obtained, which is a special case of failing to converge.
A draft toolchain roadmap is now available, laying out a compatibility plan including Python versions, C standards, and NumPy versions.
A total of 97 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.
tylerjereddy released this
May 9, 2019
· 63 commits to master since this release
Note: Scipy 1.3.0 is not released yet!
tylerjereddy released this
Apr 27, 2019
· 18 commits to master since this release
tylerjereddy released this
Feb 9, 2019
· 321 commits to master since this release
SciPy 1.2.1 is a bug-fix release with no new features compared to 1.2.0. Most importantly, it solves the issue that 1.2.0 cannot be installed from source on Python 2.7 because of non-ASCII character issues.
It is also notable that SciPy 1.2.1 wheels were built with OpenBLAS 0.3.5.dev, which may alleviate some linear algebra issues observed in SciPy 1.2.0.
tylerjereddy released this
Dec 18, 2018
· 125 commits to master since this release
SciPy 1.2.0 is the culmination of 6 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Before upgrading, we recommend that users check that their own code does not use deprecated SciPy functionality (to do so, run your code with python -Wd and check for DeprecationWarning s). Our development attention will now shift to bug-fix releases on the 1.2.x branch, and on adding new features on the master branch.
This release requires Python 2.7 or 3.4+ and NumPy 1.8.2 or greater.
Note: This will be the last SciPy release to support Python 2.7. Consequently, the 1.2.x series will be a long term support (LTS) release; we will backport bug fixes until 1 Jan 2020.
Proper spline coefficient calculations have been added for the mirror, wrap, and reflect modes of scipy.ndimage.rotate
DCT-IV, DST-IV, DCT-I, and DST-I orthonormalization are now supported in scipy.fftpack.
scipy.interpolate.pade now accepts a new argument for the order of the numerator
scipy.cluster.vq.kmeans2 gained a new initialization method, kmeans++.
The function softmax was added to scipy.special.
The one-dimensional nonlinear solvers have been given a unified interface scipy.optimize.root_scalar, similar to the scipy.optimize.root interface for multi-dimensional solvers. scipy.optimize.root_scalar(f, bracket=[a ,b], method="brenth") is equivalent to scipy.optimize.brenth(f, a ,b). If no method is specified, an appropriate one will be selected based upon the bracket and the number of derivatives available.
scipy.optimize.root_scalar(f, bracket=[a ,b], method="brenth")
scipy.optimize.brenth(f, a ,b)
The so-called Algorithm 748 of Alefeld, Potra and Shi for root-finding within an enclosing interval has been added as scipy.optimize.toms748. This provides guaranteed convergence to a root with convergence rate per function evaluation of approximately 1.65 (for sufficiently well-behaved functions.)
differential_evolution now has the updating and workers keywords. The first chooses between continuous updating of the best solution vector (the default), or once per generation. Continuous updating can lead to faster convergence. The workers keyword accepts an int or map-like callable, and parallelises the solver (having the side effect of updating once per generation). Supplying an int evaluates the trial solutions in N parallel parts. Supplying a map-like callable allows other parallelisation approaches (such as mpi4py, or joblib) to be used.
dual_annealing (and shgo below) is a powerful new general purpose global optizimation (GO) algorithm. dual_annealing uses two annealing processes to accelerate the convergence towards the global minimum of an objective mathematical function. The first annealing process controls the stochastic Markov chain searching and the second annealing process controls the deterministic minimization. So, dual annealing is a hybrid method that takes advantage of stochastic and local deterministic searching in an efficient way.
shgo (simplicial homology global optimization) is a similar algorithm appropriate for solving black box and derivative free optimization (DFO) problems. The algorithm generally converges to the global solution in finite time. The convergence holds for non-linear inequality and equality constraints. In addition to returning a global minimum, the algorithm also returns any other global and local minima found after every iteration. This makes it useful for exploring the solutions in a domain.
scipy.optimize.newton can now accept a scalar or an array
MINPACK usage is now thread-safe, such that MINPACK + callbacks may be used on multiple threads.
Digital filter design functions now include a parameter to specify the sampling rate. Previously, digital filters could only be specified using normalized frequency, but different functions used different scales (e.g. 0 to 1 for butter vs 0 to π for freqz), leading to errors and confusion. With the fs parameter, ordinary frequencies can now be entered directly into functions, with the normalization handled internally.
find_peaks and related functions no longer raise an exception if the properties of a peak have unexpected values (e.g. a prominence of 0). A PeakPropertyWarning is given instead.
The new keyword argument plateau_size was added to find_peaks. plateau_size may be used to select peaks based on the length of the flat top of a peak.
welch() and csd() methods in scipy.signal now support calculation of a median average PSD, using average='mean' keyword
The scipy.sparse.bsr_matrix.tocsr method is now implemented directly instead of converting via COO format, and the scipy.sparse.bsr_matrix.tocsc method is now also routed via CSR conversion instead of COO. The efficiency of both conversions is now improved.
The issue where SuperLU or UMFPACK solvers crashed on matrices with non-canonical format in scipy.sparse.linalg was fixed. The solver wrapper canonicalizes the matrix if necessary before calling the SuperLU or UMFPACK solver.
The largest option of scipy.sparse.linalg.lobpcg() was fixed to have a correct (and expected) behavior. The order of the eigenvalues was made consistent with the ARPACK solver (eigs()), i.e. ascending for the smallest eigenvalues, and descending for the largest eigenvalues.
The scipy.sparse.random function is now faster and also supports integer and complex values by passing the appropriate value to the dtype argument.
The function scipy.spatial.distance.jaccard was modified to return 0 instead of np.nan when two all-zero vectors are compared.
Support for the Jensen Shannon distance, the square-root of the divergence, has been added under scipy.spatial.distance.jensenshannon
An optional keyword was added to the function scipy.spatial.cKDTree.query_ball_point() to sort or not sort the returned indices. Not sorting the indices can speed up calls.
A new category of quaternion-based transformations are available in scipy.spatial.transform, including spherical linear interpolation of rotations (Slerp), conversions to and from quaternions, Euler angles, and general rotation and inversion capabilities (spatial.transform.Rotation), and uniform random sampling of 3D rotations (spatial.transform.Rotation.random).
The Yeo-Johnson power transformation is now supported (yeojohnson, yeojohnson_llf, yeojohnson_normmax, yeojohnson_normplot). Unlike the Box-Cox transformation, the Yeo-Johnson transformation can accept negative values.
Added a general method to sample random variates based on the density only, in the new function rvs_ratio_uniforms.
The Yule-Simon distribution (yulesimon) was added -- this is a new discrete probability distribution.
stats and mstats now have access to a new regression method, siegelslopes, a robust linear regression algorithm
scipy.stats.gaussian_kde now has the ability to deal with weighted samples, and should have a modest improvement in performance
Levy Stable Parameter Estimation, PDF, and CDF calculations are now supported for scipy.stats.levy_stable.
The Brunner-Munzel test is now available as brunnermunzel in stats and mstats
scipy.linalg.lapack now exposes the LAPACK routines using the Rectangular Full Packed storage (RFP) for upper triangular, lower triangular, symmetric, or Hermitian matrices; the upper trapezoidal fat matrix RZ decomposition routines are now available as well.
The functions hyp2f0, hyp1f2 and hyp3f0 in scipy.special have been deprecated.
LAPACK version 3.4.0 or later is now required. Building with Apple Accelerate is no longer supported.
The function scipy.linalg.subspace_angles(A, B) now gives correct results for all angles. Before this, the function only returned correct values for those angles which were greater than pi/4.
Support for the Bento build system has been removed. Bento has not been maintained for several years, and did not have good Python 3 or wheel support, hence it was time to remove it.
The required signature of scipy.optimize.lingprog method=simplex callback function has changed. Before iteration begins, the simplex solver first converts the problem into a standard form that does not, in general, have the same variables or constraints as the problem defined by the user. Previously, the simplex solver would pass a user-specified callback function several separate arguments, such as the current solution vector xk, corresponding to this standard form problem. Unfortunately, the relationship between the standard form problem and the user-defined problem was not documented, limiting the utility of the information passed to the callback function.
In addition to numerous bug fix changes, the simplex solver now passes a user-specified callback function a single OptimizeResult object containing information that corresponds directly to the user-defined problem. In future releases, this OptimizeResult object may be expanded to include additional information, such as variables corresponding to the standard-form problem and information concerning the relationship between the standard-form and user-defined problems.
The implementation of scipy.sparse.random has changed, and this affects the numerical values returned for both sparse.random and sparse.rand for some matrix shapes and a given seed.
scipy.optimize.newton will no longer use Halley's method in cases where it negatively impacts convergence
A total of 137 people contributed to this release. People with a "+" by their names contributed a patch for the first time. This list of names is automatically generated, and may not be fully complete.
tylerjereddy released this
Dec 5, 2018
· 86 commits to master since this release
Note: Scipy 1.2.0 is not released yet!
.. note:: This will be the last SciPy release to support Python 2.7. Consequently, the 1.2.x series will be a long term support (LTS) release; we will backport bug fixes until 1 Jan 2020.
tylerjereddy released this
Nov 26, 2018
· 51 commits to master since this release
pv released this
May 5, 2018
· 67 commits to master since this release
SciPy 1.1.0 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. Before upgrading, we recommend that users check that their own code does not use deprecated SciPy functionality (to do so, run your code with python -Wd and check for DeprecationWarning s). Our development attention will now shift to bug-fix releases on the 1.1.x branch, and on adding new features on the master branch.
This release has improved but not necessarily 100% compatibility with the PyPy Python implementation. For running on PyPy, PyPy 6.0+ and Numpy 1.15.0+ are required.
The argument tfirst has been added to the function scipy.integrate.odeint. This allows odeint to use the same user functions as scipy.integrate.solve_ivp and scipy.integrate.ode without the need for wrapping them in a function that swaps the first two arguments.
Error messages from quad() are now clearer.
The function scipy.linalg.ldl has been added for factorization of indefinite symmetric/hermitian matrices into triangular and block diagonal matrices.
Python wrappers for LAPACK sygst, hegst added in scipy.linalg.lapack.
Added scipy.linalg.null_space, scipy.linalg.cdf2rdf, scipy.linalg.rsf2csf.
An electrocardiogram has been added as an example dataset for a one-dimensional signal. It can be accessed through scipy.misc.electrocardiogram.
The routines scipy.ndimage.binary_opening, and scipy.ndimage.binary_closing now support masks and different border values.
The method trust-constr has been added to scipy.optimize.minimize. The method switches between two implementations depending on the problem definition. For equality constrained problems it is an implementation of a trust-region sequential quadratic programming solver and, when inequality constraints are imposed, it switches to a trust-region interior point method. Both methods are appropriate for large scale problems. Quasi-Newton options BFGS and SR1 were implemented and can be used to approximate second order derivatives for this new method. Also, finite-differences can be used to approximate either first-order or second-order derivatives.
Random-to-Best/1/bin and Random-to-Best/1/exp mutation strategies were added to scipy.optimize.differential_evolution as randtobest1bin and randtobest1exp, respectively. Note: These names were already in use but implemented a different mutation strategy. See Backwards incompatible changes, below. The init keyword for the scipy.optimize.differential_evolution function can now accept an array. This array allows the user to specify the entire population.
Add an adaptive option to Nelder-Mead to use step parameters adapted to the dimensionality of the problem.
Minor improvements in scipy.optimize.basinhopping.
Three new functions for peak finding in one-dimensional arrays were added. scipy.signal.find_peaks searches for peaks (local maxima) based on simple value comparison of neighbouring samples and returns those peaks whose properties match optionally specified conditions for their height, prominence, width, threshold and distance to each other. scipy.signal.peak_prominences and scipy.signal.peak_widths can directly calculate the prominences or widths of known peaks.
Added ZPK versions of frequency transformations: scipy.signal.bilinear_zpk, scipy.signal.lp2bp_zpk, scipy.signal.lp2bs_zpk, scipy.signal.lp2hp_zpk, scipy.signal.lp2lp_zpk.
Added scipy.signal.windows.dpss, scipy.signal.windows.general_cosine and scipy.signal.windows.general_hamming.
Previously, the reshape method only worked on scipy.sparse.lil_matrix, and in-place reshaping did not work on any matrices. Both operations are now implemented for all matrices. Handling of shapes has been made consistent with numpy.matrix throughout the scipy.sparse module (shape can be a tuple or splatted, negative number acts as placeholder, padding and unpadding dimensions of size 1 to ensure length-2 shape).
Added Owen's T function as scipy.special.owens_t.
Accuracy improvements in chndtr, digamma, gammaincinv, lambertw, zetac.
The Moyal distribution has been added as scipy.stats.moyal.
Added the normal inverse Gaussian distribution as scipy.stats.norminvgauss.
The iterative linear equation solvers in scipy.sparse.linalg had a sub-optimal way of how absolute tolerance is considered. The default behavior will be changed in a future Scipy release to a more standard and less surprising one. To silence deprecation warnings, set the atol= parameter explicitly.
scipy.signal.windows.slepian is deprecated, replaced by scipy.signal.windows.dpss.
The window functions in scipy.signal are now available in scipy.signal.windows. They will remain also available in the old location in the scipy.signal namespace in future Scipy versions. However, importing them from scipy.signal.windows is preferred, and new window functions will be added only there.
Indexing sparse matrices with floating-point numbers instead of integers is deprecated.
The function scipy.stats.itemfreq is deprecated.
Previously, scipy.linalg.orth used a singular value cutoff value appropriate for double precision numbers also for single-precision input. The cutoff value is now tunable, and the default has been changed to depend on the input data precision.
In previous versions of Scipy, the randtobest1bin and randtobest1exp mutation strategies in scipy.optimize.differential_evolution were actually implemented using the Current-to-Best/1/bin and Current-to-Best/1/exp strategies, respectively. These strategies were renamed to currenttobest1bin and currenttobest1exp and the implementations of randtobest1bin and randtobest1exp strategies were corrected.
Functions in the ndimage module now always return their output array. Before this most functions only returned the output array if it had been allocated by the function, and would return None if it had been provided by the user.
Distance metrics in scipy.spatial.distance now require non-negative weights.
scipy.special.loggamma returns now real-valued result when the input is real-valued.
When building on Linux with GNU compilers, the .so Python extension files now hide all symbols except those required by Python, which can avoid problems when embedding the Python interpreter.
A total of 122 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.
pv released this
Apr 15, 2018
· 36 commits to master since this release
Note: Scipy 1.1.0 is not released yet!
An in-place resize method has been added to all sparse matrix formats, which was only available for scipy.sparse.dok_matrix in previous releases.
rgommers released this
Mar 24, 2018
· 630 commits to master since this release
SciPy 1.0.1 is a bug-fix release with no new features compared to 1.0.0. Probably the most important change is a fix for an incompatibility between SciPy 1.0.0 and numpy.f2py in the NumPy master branch.