scipy/scipy: SciPy 1.1.0

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 <code>python -Wd</code> and check for <code>DeprecationWarning</code> 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% compatibility 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 <code>tfirst</code> 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 <code>quad()</code> 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 <code>sygst</code>, <code>hegst</code> 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 <code>trust-constr</code> 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 <code>randtobest1bin</code> and <code>randtobest1exp</code>, respectively. Note: These names were already in use but implemented a different mutation strategy. See Backwards incompatible changes, below. The <code>init</code> 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 <code>adaptive</code> 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 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. scipy.sparse improvements Previously, the <code>reshape</code> 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 <code>numpy.matrix</code> 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 <code>chndtr</code>, <code>digamma</code>, <code>gammaincinv</code>, <code>lambertw</code>, <code>zetac</code>. 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 <code>atol=</code> 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 <code>randtobest1bin</code> and <code>randtobest1exp</code> 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 <code>currenttobest1bin</code> and <code>currenttobest1exp</code> and the implementations of <code>randtobest1bin</code> and <code>randtobest1exp</code> 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 <code>None</code> 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. Other changes When building on Linux with GNU compilers, the <code>.so</code> Python extension files now hide all symbols except those required by Python, which can avoid problems when embedding the Python interpreter. Authors Saurabh Agarwal + Diogo Aguiam + Joseph Albert + Gerrit Ansmann + Jean-François B + Vahan Babayan + Alessandro Pietro Bardelli Christoph Baumgarten + Felix Berkenkamp Lilian Besson + Aditya Bharti + Matthew Brett Evgeni Burovski CJ Carey Martin Ø. Christensen + Robert Cimrman Vicky Close + Peter Cock + Philip DeBoer Jaime Fernandez del Rio Dieter Werthmüller + Tom Donoghue + Matt Dzugan + Lars G + Jacques Gaudin + Andriy Gelman + Sean Gillies + Dezmond Goff Christoph Gohlke Ralf Gommers Uri Goren + Deepak Kumar Gouda + Douglas Lessa Graciosa + Matt Haberland David Hagen Charles Harris Jordan Heemskerk + Danny Hermes + Stephan Hoyer + Theodore Hu + Jean-François B. + Mads Jensen + Jon Haitz Legarreta Gorroño + Ben Jude + Noel Kippers + Julius Bier Kirkegaard + Maria Knorps + Mikkel Kristensen + Eric Larson Kasper Primdal Lauritzen + Denis Laxalde KangWon Lee + Jan Lehky + Jackie Leng + P.L. Lim + Nikolay Mayorov Mihai Capotă + Max Mikhaylov + Mark Mikofski + Jarrod Millman Raden Muhammad + Paul Nation Andrew Nelson Nico Schlömer Joel Nothman Kyle Oman + Egor Panfilov + Nick Papior Anubhav Patel + Oleksandr Pavlyk Ilhan Polat Robert Pollak + Anant Prakash + Aman Pratik Sean Quinn + Giftlin Rajaiah + Tyler Reddy Joscha Reimer Antonio H Ribeiro + Antonio Horta Ribeiro Benjamin Rose + Fabian Rost Divakar Roy + Scott Sievert Leo Singer Sourav Singh Martino Sorbaro + Eric Stansifer + Martin Thoma Phil Tooley + Piotr Uchwat + Paul van Mulbregt Pauli Virtanen Stefan van der Walt Warren Weckesser Florian Weimer + Eric Wieser Josh Wilson Ted Ying + Evgeny Zhurko Zé Vinícius @Astrofysicus + @awakenting + @endolith @FormerPhysicist + @gaulinmp + @hugovk @ksemb + @kshitij12345 + @luzpaz + @NKrvavica + @rafalalgo + @samyak0210 + @soluwalana + @sudheerachary + @Tokixix + @tttthomasssss + @vkk800 + @xoviat @ziejcow + 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.