Releases: scipy/scipy
SciPy 1.6.0
SciPy 1.6.0 Release Notes
SciPy 1.6.0
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
1.6.x
branch, and on adding new features on the master branch.
This release requires Python 3.7+
and NumPy 1.16.5
or greater.
For running on PyPy, PyPy3 6.0+
is required.
Highlights of this release
scipy.ndimage
improvements: Fixes and ehancements to boundary extension
modes for interpolation functions. Support for complex-valued inputs in many
filtering and interpolation functions. Newgrid_mode
option for
scipy.ndimage.zoom
to enable results consistent with scikit-image's
rescale
.scipy.optimize.linprog
has fast, new methods for large, sparse problems
from theHiGHS
library.scipy.stats
improvements including new distributions, a new test, and
enhancements to existing distributions and tests
New features
scipy.special
improvements
scipy.special
now has improved support for 64-bit LAPACK
backend
scipy.odr
improvements
scipy.odr
now has support for 64-bit integer BLAS
scipy.odr.ODR
has gained an optional overwrite
argument so that existing
files may be overwritten.
scipy.integrate
improvements
Some renames of functions with poor names were done, with the old names
retained without being in the reference guide for backwards compatibility
reasons:
integrate.simps
was renamed tointegrate.simpson
integrate.trapz
was renamed tointegrate.trapezoid
integrate.cumtrapz
was renamed tointegrate.cumulative_trapezoid
scipy.cluster
improvements
scipy.cluster.hierarchy.DisjointSet
has been added for incremental
connectivity queries.
scipy.cluster.hierarchy.dendrogram
return value now also includes leaf color
information in leaves_color_list
.
scipy.interpolate
improvements
scipy.interpolate.interp1d
has a new method nearest-up
, similar to the
existing method nearest
but rounds half-integers up instead of down.
scipy.io
improvements
Support has been added for reading arbitrary bit depth integer PCM WAV files
from 1- to 32-bit, including the commonly-requested 24-bit depth.
scipy.linalg
improvements
The new function scipy.linalg.matmul_toeplitz
uses the FFT to compute the
product of a Toeplitz matrix with another matrix.
scipy.linalg.sqrtm
and scipy.linalg.logm
have performance improvements
thanks to additional Cython code.
Python LAPACK
wrappers have been added for pptrf
, pptrs
, ppsv
,
pptri
, and ppcon
.
scipy.linalg.norm
and the svd
family of functions will now use 64-bit
integer backends when available.
scipy.ndimage
improvements
scipy.ndimage.convolve
, scipy.ndimage.correlate
and their 1d counterparts
now accept both complex-valued images and/or complex-valued filter kernels. All
convolution-based filters also now accept complex-valued inputs
(e.g. gaussian_filter
, uniform_filter
, etc.).
Multiple fixes and enhancements to boundary handling were introduced to
scipy.ndimage
interpolation functions (i.e. affine_transform
,
geometric_transform
, map_coordinates
, rotate
, shift
, zoom
).
A new boundary mode, grid-wrap
was added which wraps images periodically,
using a period equal to the shape of the input image grid. This is in contrast
to the existing wrap
mode which uses a period that is one sample smaller
than the original signal extent along each dimension.
A long-standing bug in the reflect
boundary condition has been fixed and
the mode grid-mirror
was introduced as a synonym for reflect
.
A new boundary mode, grid-constant
is now available. This is similar to
the existing ndimage constant
mode, but interpolation will still performed
at coordinate values outside of the original image extent. This
grid-constant
mode is consistent with OpenCV's BORDER_CONSTANT
mode
and scikit-image's constant
mode.
Spline pre-filtering (used internally by ndimage
interpolation functions
when order >= 2
), now supports all boundary modes rather than always
defaulting to mirror boundary conditions. The standalone functions
spline_filter
and spline_filter1d
have analytical boundary conditions
that match modes mirror
, grid-wrap
and reflect
.
scipy.ndimage
interpolation functions now accept complex-valued inputs. In
this case, the interpolation is applied independently to the real and
imaginary components.
The ndimage
tutorials
(https://docs.scipy.org/doc/scipy/reference/tutorial/ndimage.html) have been
updated with new figures to better clarify the exact behavior of all of the
interpolation boundary modes.
scipy.ndimage.zoom
now has a grid_mode
option that changes the coordinate
of the center of the first pixel along an axis from 0 to 0.5. This allows
resizing in a manner that is consistent with the behavior of scikit-image's
resize
and rescale
functions (and OpenCV's cv2.resize
).
scipy.optimize
improvements
scipy.optimize.linprog
has fast, new methods for large, sparse problems from
the HiGHS
C++ library. method='highs-ds'
uses a high performance dual
revised simplex implementation (HSOL), method='highs-ipm'
uses an
interior-point method with crossover, and method='highs'
chooses between
the two automatically. These methods are typically much faster and often exceed
the accuracy of other linprog
methods, so we recommend explicitly
specifying one of these three method values when using linprog
.
scipy.optimize.quadratic_assignment
has been added for approximate solution
of the quadratic assignment problem.
scipy.optimize.linear_sum_assignment
now has a substantially reduced overhead
for small cost matrix sizes
scipy.optimize.least_squares
has improved performance when the user provides
the jacobian as a sparse jacobian already in csr_matrix
format
scipy.optimize.linprog
now has an rr_method
argument for specification
of the method used for redundancy handling, and a new method for this purpose
is available based on the interpolative decomposition approach.
scipy.signal
improvements
scipy.signal.gammatone
has been added to design FIR or IIR filters that
model the human auditory system.
scipy.signal.iircomb
has been added to design IIR peaking/notching comb
filters that can boost/attenuate a frequency from a signal.
scipy.signal.sosfilt
performance has been improved to avoid some previously-
observed slowdowns
scipy.signal.windows.taylor
has been added--the Taylor window function is
commonly used in radar digital signal processing
scipy.signal.gauss_spline
now supports list
type input for consistency
with other related SciPy functions
scipy.signal.correlation_lags
has been added to allow calculation of the lag/
displacement indices array for 1D cross-correlation.
scipy.sparse
improvements
A solver for the minimum weight full matching problem for bipartite graphs,
also known as the linear assignment problem, has been added in
scipy.sparse.csgraph.min_weight_full_bipartite_matching
. In particular, this
provides functionality analogous to that of
scipy.optimize.linear_sum_assignment
, but with improved performance for sparse
inputs, and the ability to handle inputs whose dense representations would not
fit in memory.
The time complexity of scipy.sparse.block_diag
has been improved dramatically
from quadratic to linear.
scipy.sparse.linalg
improvements
The vendored version of SuperLU
has been updated
scipy.fft
improvements
The vendored pocketfft
library now supports compiling with ARM neon vector
extensions and has improved thread pool behavior.
scipy.spatial
improvements
The python implementation of KDTree
has been dropped and KDTree
is now
implemented in terms of cKDTree
. You can now expect cKDTree
-like
performance by default. This also means sys.setrecursionlimit
no longer
needs to be increased for querying large trees.
transform.Rotation
has been updated with support for Modified Rodrigues
Parameters alongside the existing rotation representations (PR gh-12667).
scipy.spatial.transform.Rotation
has been partially cythonized, with some
performance improvements observed
scipy.spatial.distance.cdist
has improved performance with the minkowski
metric, especially for p-norm values of 1 or 2.
scipy.stats
improvements
New distributions have been added to scipy.stats
:
- The asymmetric Laplace continuous distribution has been added as
scipy.stats.laplace_asymmetric
. - The negative hypergeometric distribution has been added as `scipy....
SciPy 1.6.0rc2
SciPy 1.6.0 Release Notes
note: Scipy 1.6.0
is not released yet!
SciPy 1.6.0
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
1.6.x
branch, and on adding new features on the master branch.
This release requires Python 3.7
+ and NumPy 1.16.5
or greater.
For running on PyPy, PyPy3 6.0
+ is required.
Highlights of this release
scipy.ndimage
improvements: Fixes and ehancements to boundary extension
modes for interpolation functions. Support for complex-valued inputs in many
filtering and interpolation functions. Newgrid_mode
option for
scipy.ndimage.zoom
to enable results consistent with scikit-image's
rescale
.scipy.optimize.linprog
has fast, new methods for large, sparse problems
from theHiGHS
library.scipy.stats
improvements including new distributions, a new test, and
enhancements to existing distributions and tests
New features
scipy.special
improvements
scipy.special
now has improved support for 64-bit LAPACK
backend
scipy.odr
improvements
scipy.odr
now has support for 64-bit integer BLAS
scipy.odr.ODR
has gained an optional overwrite
argument so that existing
files may be overwritten.
scipy.integrate
improvements
Some renames of functions with poor names were done, with the old names
retained without being in the reference guide for backwards compatibility
reasons:
integrate.simps
was renamed tointegrate.simpson
integrate.trapz
was renamed tointegrate.trapezoid
integrate.cumtrapz
was renamed tointegrate.cumulative_trapezoid
scipy.cluster
improvements
scipy.cluster.hierarchy.DisjointSet
has been added for incremental
connectivity queries.
scipy.cluster.hierarchy.dendrogram
return value now also includes leaf color
information in leaves_color_list
.
scipy.interpolate
improvements
scipy.interpolate.interp1d
has a new method nearest-up
, similar to the
existing method nearest
but rounds half-integers up instead of down.
scipy.io
improvements
Support has been added for reading arbitrary bit depth integer PCM WAV files
from 1- to 32-bit, including the commonly-requested 24-bit depth.
scipy.linalg
improvements
The new function scipy.linalg.matmul_toeplitz
uses the FFT to compute the
product of a Toeplitz matrix with another matrix.
scipy.linalg.sqrtm
and scipy.linalg.logm
have performance improvements
thanks to additional Cython code.
Python LAPACK
wrappers have been added for pptrf
, pptrs
, ppsv
,
pptri
, and ppcon
.
scipy.linalg.norm
and the svd
family of functions will now use 64-bit
integer backends when available.
scipy.ndimage
improvements
scipy.ndimage.convolve
, scipy.ndimage.correlate
and their 1d counterparts
now accept both complex-valued images and/or complex-valued filter kernels. All
convolution-based filters also now accept complex-valued inputs
(e.g. gaussian_filter
, uniform_filter
, etc.).
Multiple fixes and enhancements to boundary handling were introduced to
scipy.ndimage
interpolation functions (i.e. affine_transform
,
geometric_transform
, map_coordinates
, rotate
, shift
, zoom
).
A new boundary mode, grid-wrap
was added which wraps images periodically,
using a period equal to the shape of the input image grid. This is in contrast
to the existing wrap
mode which uses a period that is one sample smaller
than the original signal extent along each dimension.
A long-standing bug in the reflect
boundary condition has been fixed and
the mode grid-mirror
was introduced as a synonym for reflect
.
A new boundary mode, grid-constant
is now available. This is similar to
the existing ndimage constant
mode, but interpolation will still performed
at coordinate values outside of the original image extent. This
grid-constant
mode is consistent with OpenCV's BORDER_CONSTANT
mode
and scikit-image's constant
mode.
Spline pre-filtering (used internally by ndimage
interpolation functions
when order >= 2
), now supports all boundary modes rather than always
defaulting to mirror boundary conditions. The standalone functions
spline_filter
and spline_filter1d
have analytical boundary conditions
that match modes mirror
, grid-wrap
and reflect
.
scipy.ndimage
interpolation functions now accept complex-valued inputs. In
this case, the interpolation is applied independently to the real and
imaginary components.
The ndimage
tutorials
(https://docs.scipy.org/doc/scipy/reference/tutorial/ndimage.html) have been
updated with new figures to better clarify the exact behavior of all of the
interpolation boundary modes.
scipy.ndimage.zoom
now has a grid_mode
option that changes the coordinate
of the center of the first pixel along an axis from 0 to 0.5. This allows
resizing in a manner that is consistent with the behavior of scikit-image's
resize
and rescale
functions (and OpenCV's cv2.resize
).
scipy.optimize
improvements
scipy.optimize.linprog
has fast, new methods for large, sparse problems from
the HiGHS
C++ library. method='highs-ds'
uses a high performance dual
revised simplex implementation (HSOL), method='highs-ipm'
uses an
interior-point method with crossover, and method='highs'
chooses between
the two automatically. These methods are typically much faster and often exceed
the accuracy of other linprog
methods, so we recommend explicitly
specifying one of these three method values when using linprog
.
scipy.optimize.quadratic_assignment
has been added for approximate solution
of the quadratic assignment problem.
scipy.optimize.linear_sum_assignment
now has a substantially reduced overhead
for small cost matrix sizes
scipy.optimize.least_squares
has improved performance when the user provides
the jacobian as a sparse jacobian already in csr_matrix
format
scipy.optimize.linprog
now has an rr_method
argument for specification
of the method used for redundancy handling, and a new method for this purpose
is available based on the interpolative decomposition approach.
scipy.signal
improvements
scipy.signal.gammatone
has been added to design FIR or IIR filters that
model the human auditory system.
scipy.signal.iircomb
has been added to design IIR peaking/notching comb
filters that can boost/attenuate a frequency from a signal.
scipy.signal.sosfilt
performance has been improved to avoid some previously-
observed slowdowns
scipy.signal.windows.taylor
has been added--the Taylor window function is
commonly used in radar digital signal processing
scipy.signal.gauss_spline
now supports list
type input for consistency
with other related SciPy functions
scipy.signal.correlation_lags
has been added to allow calculation of the lag/
displacement indices array for 1D cross-correlation.
scipy.sparse
improvements
A solver for the minimum weight full matching problem for bipartite graphs,
also known as the linear assignment problem, has been added in
scipy.sparse.csgraph.min_weight_full_bipartite_matching
. In particular, this
provides functionality analogous to that of
scipy.optimize.linear_sum_assignment
, but with improved performance for sparse
inputs, and the ability to handle inputs whose dense representations would not
fit in memory.
The time complexity of scipy.sparse.block_diag
has been improved dramatically
from quadratic to linear.
scipy.sparse.linalg
improvements
The vendored version of SuperLU
has been updated
scipy.fft
improvements
The vendored pocketfft
library now supports compiling with ARM neon vector
extensions and has improved thread pool behavior.
scipy.spatial
improvements
The python implementation of KDTree
has been dropped and KDTree
is now
implemented in terms of cKDTree
. You can now expect cKDTree
-like
performance by default. This also means sys.setrecursionlimit
no longer
needs to be increased for querying large trees.
transform.Rotation
has been updated with support for Modified Rodrigues
Parameters alongside the existing rotation representations (PR gh-12667).
scipy.spatial.transform.Rotation
has been partially cythonized, with some
performance improvements observed
scipy.spatial.distance.cdist
has improved performance with the minkowski
metric, especially for p-norm values of 1 or 2.
scipy.stats
improvements
New distributions have been added to scipy.stats
:
- The asymmetric Laplace continuous distribution has been added as
scipy.stats.laplace_asymmetric
. - The negative...
SciPy 1.6.0rc1
SciPy 1.6.0 Release Notes
Note: Scipy 1.6.0
is not released yet!
SciPy 1.6.0
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
1.6.x branch, and on adding new features on the master branch.
This release requires Python 3.7
+ and NumPy 1.16.5
or greater.
For running on PyPy, PyPy3 6.0
+ is required.
Highlights of this release
scipy.ndimage
improvements: Fixes and ehancements to boundary extension
modes for interpolation functions. Support for complex-valued inputs in many
filtering and interpolation functions. Newgrid_mode
option for
scipy.ndimage.zoom
to enable results consistent with scikit-image's
rescale
.scipy.optimize.linprog
has fast, new methods for large, sparse problems
from theHiGHS
library.scipy.stats
improvements including new distributions, a new test, and
enhancements to existing distributions and tests
New features
scipy.special
improvements
scipy.special
now has improved support for 64-bit LAPACK
backend
scipy.odr
improvements
scipy.odr
now has support for 64-bit integer BLAS
scipy.odr.ODR
has gained an optional overwrite
argument so that existing
files may be overwritten.
scipy.integrate
improvements
Some renames of functions with poor names were done, with the old names
retained without being in the reference guide for backwards compatibility
reasons:
integrate.simps
was renamed tointegrate.simpson
integrate.trapz
was renamed tointegrate.trapezoid
integrate.cumtrapz
was renamed tointegrate.cumulative_trapezoid
scipy.cluster
improvements
scipy.cluster.hierarchy.DisjointSet
has been added for incremental
connectivity queries.
scipy.cluster.hierarchy.dendrogram
return value now also includes leaf color
information in leaves_color_list
.
scipy.interpolate
improvements
scipy.interpolate.interp1d
has a new method nearest-up
, similar to the
existing method nearest
but rounds half-integers up instead of down.
scipy.io
improvements
Support has been added for reading arbitrary bit depth integer PCM WAV files
from 1- to 32-bit, including the commonly-requested 24-bit depth.
scipy.linalg
improvements
The new function scipy.linalg.matmul_toeplitz
uses the FFT to compute the
product of a Toeplitz matrix with another matrix.
scipy.linalg.sqrtm
and scipy.linalg.logm
have performance improvements
thanks to additional Cython code.
Python LAPACK
wrappers have been added for pptrf
, pptrs
, ppsv
,
pptri
, and ppcon
.
scipy.linalg.norm
and the svd
family of functions will now use 64-bit
integer backends when available.
scipy.ndimage
improvements
scipy.ndimage.convolve
, scipy.ndimage.correlate
and their 1d counterparts
now accept both complex-valued images and/or complex-valued filter kernels. All
convolution-based filters also now accept complex-valued inputs
(e.g. gaussian_filter
, uniform_filter
, etc.).
Multiple fixes and enhancements to boundary handling were introduced to
scipy.ndimage
interpolation functions (i.e. affine_transform
,
geometric_transform
, map_coordinates
, rotate
, shift
, zoom
).
A new boundary mode, grid-wrap
was added which wraps images periodically,
using a period equal to the shape of the input image grid. This is in contrast
to the existing wrap
mode which uses a period that is one sample smaller
than the original signal extent along each dimension.
A long-standing bug in the reflect
boundary condition has been fixed and
the mode grid-mirror
was introduced as a synonym for reflect
.
A new boundary mode, grid-constant
is now available. This is similar to
the existing ndimage constant
mode, but interpolation will still performed
at coordinate values outside of the original image extent. This
grid-constant
mode is consistent with OpenCV's BORDER_CONSTANT
mode
and scikit-image's constant
mode.
Spline pre-filtering (used internally by ndimage
interpolation functions
when order >= 2
), now supports all boundary modes rather than always
defaulting to mirror boundary conditions. The standalone functions
spline_filter
and spline_filter1d
have analytical boundary conditions
that match modes mirror
, grid-wrap
and reflect
.
scipy.ndimage
interpolation functions now accept complex-valued inputs. In
this case, the interpolation is applied independently to the real and
imaginary components.
The ndimage
tutorials
(https://docs.scipy.org/doc/scipy/reference/tutorial/ndimage.html) have been
updated with new figures to better clarify the exact behavior of all of the
interpolation boundary modes.
scipy.ndimage.zoom
now has a grid_mode
option that changes the coordinate
of the center of the first pixel along an axis from 0 to 0.5. This allows
resizing in a manner that is consistent with the behavior of scikit-image's
resize
and rescale
functions (and OpenCV's cv2.resize
).
scipy.optimize
improvements
scipy.optimize.linprog
has fast, new methods for large, sparse problems from
the HiGHS
C++ library. method='highs-ds'
uses a high performance dual
revised simplex implementation (HSOL), method='highs-ipm'
uses an
interior-point method with crossover, and method='highs'
chooses between
the two automatically. These methods are typically much faster and often exceed
the accuracy of other linprog
methods, so we recommend explicitly
specifying one of these three method values when using linprog
.
scipy.optimize.quadratic_assignment
has been added for approximate solution
of the quadratic assignment problem.
scipy.optimize.linear_sum_assignment
now has a substantially reduced overhead
for small cost matrix sizes
scipy.optimize.least_squares
has improved performance when the user provides
the jacobian as a sparse jacobian already in csr_matrix
format
scipy.optimize.linprog
now has an rr_method
argument for specification
of the method used for redundancy handling, and a new method for this purpose
is available based on the interpolative decomposition approach.
scipy.signal
improvements
scipy.signal.gammatone
has been added to design FIR or IIR filters that
model the human auditory system.
scipy.signal.iircomb
has been added to design IIR peaking/notching comb
filters that can boost/attenuate a frequency from a signal.
scipy.signal.sosfilt
performance has been improved to avoid some previously-
observed slowdowns
scipy.signal.windows.taylor
has been added--the Taylor window function is
commonly used in radar digital signal processing
scipy.signal.gauss_spline
now supports list
type input for consistency
with other related SciPy functions
scipy.signal.correlation_lags
has been added to allow calculation of the lag/
displacement indices array for 1D cross-correlation.
scipy.sparse
improvements
A solver for the minimum weight full matching problem for bipartite graphs,
also known as the linear assignment problem, has been added in
scipy.sparse.csgraph.min_weight_full_bipartite_matching
. In particular, this
provides functionality analogous to that of
scipy.optimize.linear_sum_assignment
, but with improved performance for sparse
inputs, and the ability to handle inputs whose dense representations would not
fit in memory.
The time complexity of scipy.sparse.block_diag
has been improved dramatically
from quadratic to linear.
scipy.sparse.linalg
improvements
The vendored version of SuperLU
has been updated
scipy.fft
improvements
The vendored pocketfft
library now supports compiling with ARM neon vector
extensions and has improved thread pool behavior.
scipy.spatial
improvements
The python implementation of KDTree
has been dropped and KDTree
is now
implemented in terms of cKDTree
. You can now expect cKDTree
-like
performance by default. This also means sys.setrecursionlimit
no longer
needs to be increased for querying large trees.
transform.Rotation
has been updated with support for Modified Rodrigues
Parameters alongside the existing rotation representations (PR gh-12667).
scipy.spatial.transform.Rotation
has been partially cythonized, with some
performance improvements observed
scipy.spatial.distance.cdist
has improved performance with the minkowski
metric, especially for p-norm values of 1 or 2.
scipy.stats
improvements
New distributions have been added to scipy.stats
:
- The asymmetric Laplace continuous distribution has been added as
scipy.stats.laplace_asymmetric
. - The negative hypergeometric distribution has been added as `scipy...
SciPy 1.5.4
SciPy 1.5.4 Release Notes
SciPy 1.5.4
is a bug-fix release with no new features
compared to 1.5.3
. Importantly, wheels are now available
for Python 3.9
and a more complete fix has been applied for
issues building with XCode 12
.
Authors
- Peter Bell
- CJ Carey
- Andrew McCluskey +
- Andrew Nelson
- Tyler Reddy
- Eli Rykoff +
- Ian Thomas +
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.
SciPy 1.5.3
SciPy 1.5.3 Release Notes
SciPy 1.5.3
is a bug-fix release with no new features
compared to 1.5.2
. In particular, Linux ARM64 wheels are now
available and a compatibility issue with XCode 12 has
been fixed.
Authors
- Peter Bell
- CJ Carey
- Thomas Duvernay +
- Gregory Lee
- Eric Moore
- odidev
- Dima Pasechnik
- Tyler Reddy
- Simon Segerblom Rex +
- Daniel B. Smith
- Will Tirone +
- Warren Weckesser
A total of 12 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.
SciPy 1.5.2
SciPy 1.5.2 Release Notes
SciPy 1.5.2
is a bug-fix release with no new features
compared to 1.5.1
.
Authors
- Peter Bell
- Tobias Biester +
- Evgeni Burovski
- Thomas A Caswell
- Ralf Gommers
- Sturla Molden
- Andrew Nelson
- ofirr +
- Sambit Panda
- Ilhan Polat
- Tyler Reddy
- Atsushi Sakai
- Pauli Virtanen
A total of 13 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.
SciPy 1.5.1
SciPy 1.5.1 Release Notes
SciPy 1.5.1
is a bug-fix release with no new features
compared to 1.5.0
. In particular, an issue where DLL loading
can fail for SciPy wheels on Windows with Python 3.6
has been
fixed.
Authors
- Peter Bell
- Loïc Estève
- Philipp Thölke +
- Tyler Reddy
- Paul van Mulbregt
- Pauli Virtanen
- Warren Weckesser
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.
SciPy 1.5.0
SciPy 1.5.0 Release Notes
SciPy 1.5.0
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
1.5.x branch, and on adding new features on the master branch.
This release requires Python 3.6+
and NumPy 1.14.5
or greater.
For running on PyPy, PyPy3 6.0+
and NumPy 1.15.0
are required.
Highlights of this release
- wrappers for more than a dozen new
LAPACK
routines are now available
inscipy.linalg.lapack
- Improved support for leveraging 64-bit integer size from linear algebra
backends - addition of the probability distribution for two-sided one-sample
Kolmogorov-Smirnov tests
New features
scipy.cluster
improvements
Initialization of scipy.cluster.vq.kmeans2
using minit="++"
had a
quadratic complexity in the number of samples. It has been improved, resulting
in a much faster initialization with quasi-linear complexity.
scipy.cluster.hierarchy.dendrogram
now respects the matplotlib
color
palette
scipy.fft
improvements
A new keyword-only argument plan
is added to all FFT functions in this
module. It is reserved for passing in a precomputed plan from libraries
providing a FFT backend (such as PyFFTW
and mkl-fft
), and it is
currently not used in SciPy.
scipy.integrate
improvements
scipy.interpolate
improvements
scipy.io
improvements
scipy.io.wavfile
error messages are more explicit about what's wrong, and
extraneous bytes at the ends of files are ignored instead of raising an error
when the data has successfully been read.
scipy.io.loadmat
gained a simplify_cells
parameter, which if set to
True
simplifies the structure of the return value if the .mat
file
contains cell arrays.
pathlib.Path
objects are now supported in scipy.io
Matrix Market I/O
functions
scipy.linalg
improvements
scipy.linalg.eigh
has been improved. Now various LAPACK
drivers can be
selected at will and also subsets of eigenvalues can be requested via
subset_by_value
keyword. Another keyword subset_by_index
is introduced.
Keywords turbo
and eigvals
are deprecated.
Similarly, standard and generalized Hermitian eigenvalue LAPACK
routines
?<sy/he>evx
are added and existing ones now have full _lwork
counterparts.
Wrappers for the following LAPACK
routines have been added to
scipy.linalg.lapack
:
?getc2
: computes the LU factorization of a general matrix with complete
pivoting?gesc2
: solves a linear system given an LU factorization from?getc2
?gejsv
: computes the singular value decomposition of a general matrix
with higher accuracy calculation of tiny singular values and their
corresponding singular vectors?geqrfp
: computes the QR factorization of a general matrix with
non-negative elements on the diagonal of R?gtsvx
: solves a linear system with general tridiagonal matrix?gttrf
: computes the LU factorization of a tridiagonal matrix?gttrs
: solves a linear system given an LU factorization from?gttrf
?ptsvx
: solves a linear system with symmetric positive definite
tridiagonal matrix?pttrf
: computes the LU factorization of a symmetric positive definite
tridiagonal matrix?pttrs
: solves a linear system given an LU factorization from?pttrf
?pteqr
: computes the eigenvectors and eigenvalues of a positive definite
tridiagonal matrix?tbtrs
: solves a linear system with a triangular banded matrix?csd
: computes the Cosine Sine decomposition of an orthogonal/unitary
matrix
Generalized QR factorization routines (?geqrf
) now have full _lwork
counterparts.
scipy.linalg.cossin
Cosine Sine decomposition of unitary matrices has been
added.
The function scipy.linalg.khatri_rao
, which computes the Khatri-Rao product,
was added.
The new function scipy.linalg.convolution_matrix
constructs the Toeplitz
matrix representing one-dimensional convolution.
scipy.ndimage
improvements
scipy.optimize
improvements
The finite difference numerical differentiation used in various minimize
methods that use gradients has several new features:
- 2-point, 3-point, or complex step finite differences can be used. Previously
only a 2-step finite difference was available. - There is now the possibility to use a relative step size, previously only an
absolute step size was available. - If the
minimize
method uses bounds the numerical differentiation strictly
obeys those limits. - The numerical differentiation machinery now makes use of a simple cache,
which in some cases can reduce the number of function evaluations. minimize
'smethod= 'powell'
now supports simple bound constraints
There have been several improvements to scipy.optimize.linprog
:
- The
linprog
benchmark suite has been expanded considerably. linprog
's dense pivot-based redundancy removal routine and sparse
presolve are faster- When
scikit-sparse
is available, solving sparse problems with
method='interior-point'
is faster
The caching of values when optimizing a function returning both value and
gradient together has been improved, avoiding repeated function evaluations
when using a HessianApproximation
such as BFGS
.
differential_evolution
can now use the modern np.random.Generator
as
well as the legacy np.random.RandomState
as a seed.
scipy.signal
improvements
A new optional argument include_nyquist
is added to freqz
functions in
this module. It is used for including the last frequency (Nyquist frequency).
scipy.signal.find_peaks_cwt
now accepts a window_size
parameter for the
size of the window used to calculate the noise floor.
scipy.sparse
improvements
Outer indexing is now faster when using a 2d column vector to select column
indices.
scipy.sparse.lil.tocsr
is faster
Fixed/improved comparisons between pydata sparse arrays and sparse matrices
BSR format sparse multiplication performance has been improved.
scipy.sparse.linalg.LinearOperator
has gained the new ndim
class
attribute
scipy.spatial
improvements
scipy.spatial.geometric_slerp
has been added to enable geometric
spherical linear interpolation on an n-sphere
scipy.spatial.SphericalVoronoi
now supports calculation of region areas in 2D
and 3D cases
The tree building algorithm used by cKDTree
has improved from quadratic
worst case time complexity to loglinear. Benchmarks are also now available for
building and querying of balanced/unbalanced kd-trees.
scipy.special
improvements
The following functions now have Cython interfaces in cython_special
:
scipy.special.erfinv
scipy.special.erfcinv
scipy.special.spherical_jn
scipy.special.spherical_yn
scipy.special.spherical_in
scipy.special.spherical_kn
scipy.special.log_softmax
has been added to calculate the logarithm of softmax
function. It provides better accuracy than log(scipy.special.softmax(x))
for
inputs that make softmax saturate.
scipy.stats
improvements
The function for generating random samples in scipy.stats.dlaplace
has been
improved. The new function is approximately twice as fast with a memory
footprint reduction between 25 % and 60 % (see gh-11069).
scipy.stats
functions that accept a seed for reproducible calculations using
random number generation (e.g. random variates from distributions) can now use
the modern np.random.Generator
as well as the legacy
np.random.RandomState
as a seed.
The axis
parameter was added to scipy.stats.rankdata
. This allows slices
of an array along the given axis to be ranked independently.
The axis
parameter was added to scipy.stats.f_oneway
, allowing it to
compute multiple one-way ANOVA tests for data stored in n-dimensional
arrays. The performance of f_oneway
was also improved for some cases.
The PDF and CDF methods for stats.geninvgauss
are now significantly faster
as the numerical integration to calculate the CDF uses a Cython based
LowLevelCallable
.
Moments of the normal distribution (scipy.stats.norm
) are now calculated using
analytical formulas instead of numerical integration for greater speed and
accuracy
Moments and entropy trapezoidal distribution (scipy.stats.trapz
) are now
calculated using analytical formulas instead of numerical integration for
greater speed and accuracy
Methods of the truncated normal distribution (scipy.stats.truncnorm
),
especially _rvs
, are significantly faster after a complete rewrite.
The fit
method of the Laplace distribution, scipy.stats.laplace
, now uses
the analytical formulas for the maximum likelihood estimates of th...
SciPy 1.5.0rc2
SciPy 1.5.0 Release Notes
Note Scipy 1.5.0
is not released yet!
SciPy 1.5.0
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
1.5.x branch, and on adding new features on the master branch.
This release requires Python 3.6+
and NumPy 1.14.5
or greater.
For running on PyPy, PyPy3 6.0+
and NumPy 1.15.0
are required.
Highlights of this release
- wrappers for more than a dozen new
LAPACK
routines are now available
inscipy.linalg.lapack
- Improved support for leveraging 64-bit integer size from linear algebra
backends - addition of the probability distribution for two-sided one-sample
Kolmogorov-Smirnov tests
New features
scipy.cluster
improvements
Initialization of scipy.cluster.vq.kmeans2
using minit="++"
had a
quadratic complexity in the number of samples. It has been improved, resulting
in a much faster initialization with quasi-linear complexity.
scipy.cluster.hierarchy.dendrogram
now respects the matplotlib
color
palette
scipy.fft
improvements
A new keyword-only argument plan
is added to all FFT functions in this
module. It is reserved for passing in a precomputed plan from libraries
providing a FFT backend (such as PyFFTW
and mkl-fft
), and it is
currently not used in SciPy.
scipy.integrate
improvements
scipy.interpolate
improvements
scipy.io
improvements
scipy.io.wavfile
error messages are more explicit about what's wrong, and
extraneous bytes at the ends of files are ignored instead of raising an error
when the data has successfully been read.
scipy.io.loadmat
gained a simplify_cells
parameter, which if set to
True
simplifies the structure of the return value if the .mat
file
contains cell arrays.
pathlib.Path
objects are now supported in scipy.io
Matrix Market I/O
functions
scipy.linalg
improvements
scipy.linalg.eigh
has been improved. Now various LAPACK
drivers can be
selected at will and also subsets of eigenvalues can be requested via
subset_by_value
keyword. Another keyword subset_by_index
is introduced.
Keywords turbo
and eigvals
are deprecated.
Similarly, standard and generalized Hermitian eigenvalue LAPACK
routines
?<sy/he>evx
are added and existing ones now have full _lwork
counterparts.
Wrappers for the following LAPACK
routines have been added to
scipy.linalg.lapack
:
?getc2
: computes the LU factorization of a general matrix with complete
pivoting?gesc2
: solves a linear system given an LU factorization from?getc2
?gejsv
: computes the singular value decomposition of a general matrix
with higher accuracy calculation of tiny singular values and their
corresponding singular vectors?geqrfp
: computes the QR factorization of a general matrix with
non-negative elements on the diagonal of R?gtsvx
: solves a linear system with general tridiagonal matrix?gttrf
: computes the LU factorization of a tridiagonal matrix?gttrs
: solves a linear system given an LU factorization from?gttrf
?ptsvx
: solves a linear system with symmetric positive definite
tridiagonal matrix?pttrf
: computes the LU factorization of a symmetric positive definite
tridiagonal matrix?pttrs
: solves a linear system given an LU factorization from?pttrf
?pteqr
: computes the eigenvectors and eigenvalues of a positive definite
tridiagonal matrix?tbtrs
: solves a linear system with a triangular banded matrix?csd
: computes the Cosine Sine decomposition of an orthogonal/unitary
matrix
Generalized QR factorization routines (?geqrf
) now have full _lwork
counterparts.
scipy.linalg.cossin
Cosine Sine decomposition of unitary matrices has been
added.
The function scipy.linalg.khatri_rao
, which computes the Khatri-Rao product,
was added.
The new function scipy.linalg.convolution_matrix
constructs the Toeplitz
matrix representing one-dimensional convolution.
scipy.ndimage
improvements
scipy.optimize
improvements
The finite difference numerical differentiation used in various minimize
methods that use gradients has several new features:
- 2-point, 3-point, or complex step finite differences can be used. Previously
only a 2-step finite difference was available. - There is now the possibility to use a relative step size, previously only an
absolute step size was available. - If the
minimize
method uses bounds the numerical differentiation strictly
obeys those limits. - The numerical differentiation machinery now makes use of a simple cache,
which in some cases can reduce the number of function evaluations. minimize
'smethod= 'powell'
now supports simple bound constraints
There have been several improvements to scipy.optimize.linprog
:
- The
linprog
benchmark suite has been expanded considerably. linprog
's dense pivot-based redundancy removal routine and sparse
presolve are faster- When
scikit-sparse
is available, solving sparse problems with
method='interior-point'
is faster
The caching of values when optimizing a function returning both value and
gradient together has been improved, avoiding repeated function evaluations
when using a HessianApproximation
such as BFGS
.
differential_evolution
can now use the modern np.random.Generator
as
well as the legacy np.random.RandomState
as a seed.
scipy.signal
improvements
A new optional argument include_nyquist
is added to freqz
functions in
this module. It is used for including the last frequency (Nyquist frequency).
scipy.signal.find_peaks_cwt
now accepts a window_size
parameter for the
size of the window used to calculate the noise floor.
scipy.sparse
improvements
Outer indexing is now faster when using a 2d column vector to select column
indices.
scipy.sparse.lil.tocsr
is faster
Fixed/improved comparisons between pydata sparse arrays and sparse matrices
BSR format sparse multiplication performance has been improved.
scipy.sparse.linalg.LinearOperator
has gained the new ndim
class
attribute
scipy.spatial
improvements
scipy.spatial.geometric_slerp
has been added to enable geometric
spherical linear interpolation on an n-sphere
scipy.spatial.SphericalVoronoi
now supports calculation of region areas in 2D
and 3D cases
The tree building algorithm used by cKDTree
has improved from quadratic
worst case time complexity to loglinear. Benchmarks are also now available for
building and querying of balanced/unbalanced kd-trees.
scipy.special
improvements
The following functions now have Cython interfaces in cython_special
:
scipy.special.erfinv
scipy.special.erfcinv
scipy.special.spherical_jn
scipy.special.spherical_yn
scipy.special.spherical_in
scipy.special.spherical_kn
scipy.special.log_softmax
has been added to calculate the logarithm of softmax
function. It provides better accuracy than log(scipy.special.softmax(x))
for
inputs that make softmax saturate.
scipy.stats
improvements
The function for generating random samples in scipy.stats.dlaplace
has been
improved. The new function is approximately twice as fast with a memory
footprint reduction between 25 % and 60 % (see gh-11069).
scipy.stats
functions that accept a seed for reproducible calculations using
random number generation (e.g. random variates from distributions) can now use
the modern np.random.Generator
as well as the legacy
np.random.RandomState
as a seed.
The axis
parameter was added to scipy.stats.rankdata
. This allows slices
of an array along the given axis to be ranked independently.
The axis
parameter was added to scipy.stats.f_oneway
, allowing it to
compute multiple one-way ANOVA tests for data stored in n-dimensional
arrays. The performance of f_oneway
was also improved for some cases.
The PDF and CDF methods for stats.geninvgauss
are now significantly faster
as the numerical integration to calculate the CDF uses a Cython based
LowLevelCallable
.
Moments of the normal distribution (scipy.stats.norm
) are now calculated using
analytical formulas instead of numerical integration for greater speed and
accuracy
Moments and entropy trapezoidal distribution (scipy.stats.trapz
) are now
calculated using analytical formulas instead of numerical integration for
greater speed and accuracy
Methods of the truncated normal distribution (scipy.stats.truncnorm
),
especially _rvs
, are significantly faster after a complete rewrite.
The fit
method of the Laplace distribution, scipy.stats.laplace
, now uses
the analytical formulas for t...
SciPy 1.5.0rc1
SciPy 1.5.0 Release Notes
Note: Scipy 1.5.0
is not released yet!
SciPy 1.5.0
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
1.5.x branch, and on adding new features on the master branch.
This release requires Python 3.6+
and NumPy 1.14.5
or greater.
For running on PyPy, PyPy3 6.0+
and NumPy 1.15.0
are required.
Highlights of this release
- wrappers for more than a dozen new
LAPACK
routines are now available
inscipy.linalg.lapack
- Improved support for leveraging 64-bit integer size from linear algebra
backends - addition of the probability distribution for two-sided one-sample
Kolmogorov-Smirnov tests
New features
scipy.cluster
improvements
Initialization of scipy.cluster.vq.kmeans2
using minit="++"
had a
quadratic complexity in the number of samples. It has been improved, resulting
in a much faster initialization with quasi-linear complexity.
scipy.cluster.hierarchy.dendrogram
now respects the matplotlib
color
palette
scipy.fft
improvements
A new keyword-only argument plan
is added to all FFT functions in this
module. It is reserved for passing in a precomputed plan from libraries
providing a FFT backend (such as PyFFTW
and mkl-fft
), and it is
currently not used in SciPy.
scipy.integrate
improvements
scipy.interpolate
improvements
scipy.io
improvements
scipy.io.wavfile
error messages are more explicit about what's wrong, and
extraneous bytes at the ends of files are ignored instead of raising an error
when the data has successfully been read.
scipy.io.loadmat
gained a simplify_cells
parameter, which if set to
True
simplifies the structure of the return value if the .mat
file
contains cell arrays.
pathlib.Path
objects are now supported in scipy.io
Matrix Market I/O
functions
scipy.linalg
improvements
scipy.linalg.eigh
has been improved. Now various LAPACK
drivers can be
selected at will and also subsets of eigenvalues can be requested via
subset_by_value
keyword. Another keyword subset_by_index
is introduced.
Keywords turbo
and eigvals
are deprecated.
Similarly, standard and generalized Hermitian eigenvalue LAPACK
routines
?<sy/he>evx
are added and existing ones now have full _lwork
counterparts.
Wrappers for the following LAPACK
routines have been added to
scipy.linalg.lapack
:
?getc2
: computes the LU factorization of a general matrix with complete
pivoting?gesc2
: solves a linear system given an LU factorization from?getc2
?gejsv
: computes the singular value decomposition of a general matrix
with higher accuracy calculation of tiny singular values and their
corresponding singular vectors?geqrfp
: computes the QR factorization of a general matrix with
non-negative elements on the diagonal of R?gtsvx
: solves a linear system with general tridiagonal matrix?gttrf
: computes the LU factorization of a tridiagonal matrix?gttrs
: solves a linear system given an LU factorization from?gttrf
?ptsvx
: solves a linear system with symmetric positive definite
tridiagonal matrix?pttrf
: computes the LU factorization of a symmetric positive definite
tridiagonal matrix?pttrs
: solves a linear system given an LU factorization from?pttrf
?pteqr
: computes the eigenvectors and eigenvalues of a positive definite
tridiagonal matrix?tbtrs
: solves a linear system with a triangular banded matrix?csd
: computes the Cosine Sine decomposition of an orthogonal/unitary
matrix
Generalized QR factorization routines (?geqrf
) now have full _lwork
counterparts.
scipy.linalg.cossin
Cosine Sine decomposition of unitary matrices has been
added.
The function scipy.linalg.khatri_rao
, which computes the Khatri-Rao product,
was added.
The new function scipy.linalg.convolution_matrix
constructs the Toeplitz
matrix representing one-dimensional convolution.
scipy.ndimage
improvements
scipy.optimize
improvements
The finite difference numerical differentiation used in various minimize
methods that use gradients has several new features:
- 2-point, 3-point, or complex step finite differences can be used. Previously
only a 2-step finite difference was available. - There is now the possibility to use a relative step size, previously only an
absolute step size was available. - If the
minimize
method uses bounds the numerical differentiation strictly
obeys those limits. - The numerical differentiation machinery now makes use of a simple cache,
which in some cases can reduce the number of function evaluations. minimize
'smethod= 'powell'
now supports simple bound constraints
There have been several improvements to scipy.optimize.linprog
:
- The
linprog
benchmark suite has been expanded considerably. linprog
's dense pivot-based redundancy removal routine and sparse
presolve are faster- When
scikit-sparse
is available, solving sparse problems with
method='interior-point'
is faster
The caching of values when optimizing a function returning both value and
gradient together has been improved, avoiding repeated function evaluations
when using a HessianApproximation
such as BFGS
.
differential_evolution
can now use the modern np.random.Generator
as
well as the legacy np.random.RandomState
as a seed.
scipy.signal
improvements
A new optional argument include_nyquist
is added to freqz
functions in
this module. It is used for including the last frequency (Nyquist frequency).
scipy.signal.find_peaks_cwt
now accepts a window_size
parameter for the
size of the window used to calculate the noise floor.
scipy.sparse
improvements
Outer indexing is now faster when using a 2d column vector to select column
indices.
scipy.sparse.lil.tocsr
is faster
Fixed/improved comparisons between pydata sparse arrays and sparse matrices
BSR format sparse multiplication performance has been improved.
scipy.sparse.linalg.LinearOperator
has gained the new ndim
class
attribute
scipy.spatial
improvements
scipy.spatial.geometric_slerp
has been added to enable geometric
spherical linear interpolation on an n-sphere
scipy.spatial.SphericalVoronoi
now supports calculation of region areas in 2D
and 3D cases
The tree building algorithm used by cKDTree
has improved from quadratic
worst case time complexity to loglinear. Benchmarks are also now available for
building and querying of balanced/unbalanced kd-trees.
scipy.special
improvements
The following functions now have Cython interfaces in cython_special
:
scipy.special.erfinv
scipy.special.erfcinv
scipy.special.spherical_jn
scipy.special.spherical_yn
scipy.special.spherical_in
scipy.special.spherical_kn
scipy.special.log_softmax
has been added to calculate the logarithm of softmax
function. It provides better accuracy than log(scipy.special.softmax(x))
for
inputs that make softmax saturate.
scipy.stats
improvements
The function for generating random samples in scipy.stats.dlaplace
has been
improved. The new function is approximately twice as fast with a memory
footprint reduction between 25 % and 60 % (see gh-11069).
scipy.stats
functions that accept a seed for reproducible calculations using
random number generation (e.g. random variates from distributions) can now use
the modern np.random.Generator
as well as the legacy
np.random.RandomState
as a seed.
The axis
parameter was added to scipy.stats.rankdata
. This allows slices
of an array along the given axis to be ranked independently.
The axis
parameter was added to scipy.stats.f_oneway
, allowing it to
compute multiple one-way ANOVA tests for data stored in n-dimensional
arrays. The performance of f_oneway
was also improved for some cases.
The PDF and CDF methods for stats.geninvgauss
are now significantly faster
as the numerical integration to calculate the CDF uses a Cython based
LowLevelCallable
.
Moments of the normal distribution (scipy.stats.norm
) are now calculated using
analytical formulas instead of numerical integration for greater speed and
accuracy
Moments and entropy trapezoidal distribution (scipy.stats.trapz
) are now
calculated using analytical formulas instead of numerical integration for
greater speed and accuracy
Methods of the truncated normal distribution (scipy.stats.truncnorm
),
especially _rvs
, are significantly faster after a complete rewrite.
The fit
method of the Laplace distribution, scipy.stats.laplace
, now uses
the...