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

This release has improved but not necessarily 100% compatible with the PyPy Python implementation. For running on PyPy, PyPy 6.0+ and Numpy 1.15.0+ are required.

New features¶ scipy.integrate improvements¶ 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. scipy.linalg improvements¶ 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 . scipy.misc improvements¶ An electrocardiogram has been added as an example dataset for a one-dimensional signal. It can be accessed through scipy.misc.electrocardiogram . scipy.ndimage improvements¶ The routines scipy.ndimage.binary_opening , and scipy.ndimage.binary_closing now support masks and different border values. scipy.optimize improvements¶ 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. Added an adaptive option to Nelder-Mead to use step parameters adapted to the dimensionality of the problem. Minor improvements in scipy.optimize.basinhopping . scipy.signal improvements¶ 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 neighboring 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 . scipy.sparse improvements¶ 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). scipy.special improvements¶ Added Owen’s T function as scipy.special.owens_t . Accuracy improvements in chndtr , digamma , gammaincinv , lambertw , zetac . scipy.stats improvements¶ The Moyal distribution has been added as scipy.stats.moyal . Added the normal inverse Gaussian distribution as scipy.stats.norminvgauss .

Deprecated features¶ 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.

Backwards incompatible changes¶ 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, 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 now returns real-valued result when the input is real-valued.