SciPy 1.1.0 Release Notes#
Contents
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scipy.integrateimprovementsscipy.linalgimprovementsscipy.miscimprovementsscipy.ndimageimprovementsscipy.optimizeimprovementsscipy.signalimprovementsscipy.sparseimprovementsscipy.specialimprovementsscipy.statsimprovements
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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.
Other changes#
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.