Most of this roadmap is intended to provide a high-level view on what is most needed per SciPy submodule in terms of new functionality, bug fixes, etc. Besides important “business as usual” changes, it contains ideas for major new features - those are marked as such, and are expected to take significant dedicated effort. Things not mentioned in this roadmap are not necessarily unimportant or out of scope, however we (the SciPy developers) want to provide to our users and contributors a clear picture of where SciPy is going and where help is needed most.
This roadmap will be evolving together with SciPy. Updates can be submitted as pull requests. For large or disruptive changes you may want to discuss those first on the scipy-dev mailing list.
In general, we want to evolve the API to remove known warts as much as possible, however as much as possible without breaking backwards compatibility.
Also, it should be made (even) more clear what is public and what is private in SciPy. Everything private should be named starting with an underscore as much as possible.
Test coverage of code added in the last few years is quite good, and we aim for a high coverage for all new code that is added. However, there is still a significant amount of old code for which coverage is poor. Bringing that up to the current standard is probably not realistic, but we should plug the biggest holes.
Besides coverage there is also the issue of correctness - older code may have a
few tests that provide decent statement coverage, but that doesn’t necessarily
say much about whether the code does what it says on the box. Therefore code
review of some parts of the code (
particular) is necessary.
The documentation is in good shape. Expanding of current docstrings and putting them in the standard NumPy format should continue, so the number of reST errors and glitches in the html docs decreases. Most modules also have a tutorial in the reference guide that is a good introduction, however there are a few missing or incomplete tutorials - this should be fixed.
Regarding Cython code:
- It’s not clear how much functionality can be Cythonized without making the .so files too large. This needs measuring.
- Cython’s old syntax for using NumPy arrays should be removed and replaced with Cython memoryviews.
Regarding build environments:
- SciPy builds from source on Windows now with a MSVC + MinGW-w64 gfortran toolchain. This still needs to prove itself, but is looking good so far.
- Support for Accelerate will be dropped, likely in SciPy 1.1.0. If there is enough interest, we may want to write wrappers so the BLAS part of Accelerate can still be used.
Continuous integration is in good shape, it covers Windows, macOS and Linux, as well as a range of versions of our dependencies and building release quality wheels.
This module is in good shape.
This module is basically done, low-maintenance and without open issues.
- solve issues with single precision: large errors, disabled for difficult sizes
- fix caching bug
- Bluestein algorithm (or chirp Z-transform)
- deprecate fftpack.convolve as public function (was not meant to be public)
There’s a large overlap with
numpy.fft. This duplication has to change
(both are too widely used to deprecate one); in the documentation we should
make clear that
scipy.fftpack is preferred over
If there are differences in signature or functionality, the best version
should be picked case by case (example: numpy’s
rfft is preferred, see
Needed for ODE solvers:
- Documentation is pretty bad, needs fixing
- A new ODE solver interface (
solve_ivp) was added in SciPy 1.0.0. In the future we can consider (soft-)deprecating the older API.
The numerical integration functions are in good shape. Support for integrating complex-valued functions and integrating multiple intervals (see gh-3325) could be added.
Ideas for new features:
- Spline fitting routines with better user control.
- Integration and differentiation and arithmetic routines for splines
- Transparent tensor-product splines.
- NURBS support.
- Mesh refinement and coarsening of B-splines and corresponding tensor products.
- PCM float will be supported, for anything else use
audiolabor other specialized libraries.
- Raise errors instead of warnings if data not understood.
Other sub-modules (matlab, netcdf, idl, harwell-boeing, arff, matrix market) are in good shape.
scipy.linalg is in good shape. We have started requiring more recent
LAPACK versions (minimum version increases from 3.1.0 to 3.4.0 in SciPy 1.2.0);
we want to add support for newer features in LAPACK.
- Reduce duplication of functions with
numpy.linalg, make APIs consistent.
get_lapack_funcsshould always use
- Wrap more LAPACK functions
- One too many funcs for LU decomposition, remove one
Ideas for new features:
- Add type-generic wrappers in the Cython BLAS and LAPACK
- Make many of the linear algebra routines into gufuncs
scipy.misc will be removed as a public module. Most functions in it have
been moved to another submodule or deprecated. The few that are left:
doccer: move to
scipy._lib(making it private)
who: these are NumPy functions
central_diff_weight: remove, possibly replacing them with more extensive functionality for numerical differentiation.
ndimage is a powerful interpolation engine. Unfortunately, it was
never decided whether to use a pixel model (
(1, 1) elements with centers
(0.5, 0.5)) or a data point model (values at points on a grid). Over time,
it seems that the data point model is better defined and easier to implement.
We therefore propose to move to this data representation for 1.0, and to vet
all interpolation code to ensure that boundary values, transformations, etc.
are correctly computed. Addressing this issue will close several issues,
including #1323, #1903, #2045 and #2640.
The morphology interface needs to be standardized:
- binary dilation/erosion/opening/closing take a “structure” argument, whereas their grey equivalent take size (has to be a tuple, not a scalar), footprint, or structure.
- a scalar should be acceptable for size, equivalent to providing that same value for each axis.
- for binary dilation/erosion/opening/closing, the structuring element is optional, whereas it’s mandatory for grey. Grey morphology operations should get the same default.
- other filters should also take that default value where possible.
Rename the module to
optimize.curve_fit. This module will then provide a home for other fitting
functionality - what exactly needs to be worked out in more detail, a
discussion can be found at https://github.com/scipy/scipy/pull/448.
Overall this module is in reasonably good shape, however it is missing a few more good global optimizers as well as large-scale optimizers. These should be added. Other things that are needed:
- deprecate the
fmin_*functions in the documentation,
- clearly define what’s out of scope for this module.
Convolution and correlation: (Relevant functions are convolve, correlate,
fftconvolve, convolve2d, correlate2d, and sepfir2d.) Eliminate the overlap with
ndimage (and elsewhere). From
(and anywhere else we find them), pick the “best of class” for 1-D, 2-D and n-d
convolution and correlation, put the implementation somewhere, and use that
consistently throughout SciPy.
B-splines: (Relevant functions are bspline, cubic, quadratic, gauss_spline, cspline1d, qspline1d, cspline2d, qspline2d, cspline1d_eval, and spline_filter.) Move the good stuff to interpolate (with appropriate API changes to match how things are done in interpolate), and eliminate any duplication.
Filter design: merge firwin and firwin2 so firwin2 can be removed.
Continuous-Time Linear Systems: remove lsim2, impulse2, step2. The
lsim, impulse and step functions now “just work” for any input system.
Further improve the performance of
ltisys (fewer internal transformations
between different representations). Fill gaps in lti system conversion functions.
Second Order Sections: Make SOS filtering equally capable as existing methods. This includes ltisys objects, an lfiltic equivalent, and numerically stable conversions to and from other filter representations. SOS filters could be considered as the default filtering method for ltisys objects, for their numerical stability.
Wavelets: what’s there now doesn’t make much sense. Continuous wavelets only at the moment - decide whether to completely rewrite or remove them. Discrete wavelet transforms are out of scope (PyWavelets does a good job for those).
The sparse matrix formats are getting feature-complete but are slow … reimplement parts in Cython?
- Small matrices are slower than PySparse, needs fixing
There are a lot of formats. These should be kept, but improvements/optimizations should go into CSR/CSC, which are the preferred formats. LIL may be the exception, it’s inherently inefficient. It could be dropped if DOK is extended to support all the operations LIL currently provides. Alternatives are being worked on, see https://github.com/ev-br/sparr and https://github.com/perimosocordiae/sparray.
Ideas for new features:
- Sparse arrays now act like np.matrix. We want sparse arrays.
This module is in good shape.
Arpack is in good shape.
- callback keyword is inconsistent
- tol keyword is broken, should be relative tol
- Fortran code not re-entrant (but we don’t solve, maybe re-use from PyKrilov)
- add sparse Cholesky or incomplete Cholesky
- look at CHOLMOD
Ideas for new features:
- Wrappers for PROPACK for faster sparse SVD computation.
QHull wrappers are in good shape.
KDTreewill be removed, and
cKDTreewill be renamed to
KDTreein a backwards-compatible way.
distance_wrap.cneeds to be cleaned up (maybe rewrite in Cython).
Though there are still a lot of functions that need improvements in precision, probably the only show-stoppers are hypergeometric functions, parabolic cylinder functions, and spheroidal wave functions. Three possible ways to handle this:
- Get good double-precision implementations. This is doable for parabolic cylinder functions (in progress). I think it’s possible for hypergeometric functions, though maybe not in time. For spheroidal wavefunctions this is not possible with current theory.
- Port Boost’s arbitrary precision library and use it under the hood to get double precision accuracy. This might be necessary as a stopgap measure for hypergeometric functions; the idea of using arbitrary precision has been suggested before by @nmayorov and in gh-5349. Likely necessary for spheroidal wave functions, this could be reused: https://github.com/radelman/scattering.
- Add clear warnings to the documentation about the limits of the existing implementations.
stats.distributions is in good shape.
gaussian_kde is in good shape but limited. It should not be expanded
probably, this fits better in Statsmodels (which already has a lot more KDE
New modules under discussion¶
Currently Scipy doesn’t provide much support for numerical differentiation.
scipy.diff module for that is discussed in
https://github.com/scipy/scipy/issues/2035. There’s also a fairly detailed
GSoC proposal to build on, see here.
There has been a second (unsuccessful) GSoC project in 2017. Recent discussion
and the host of alternatives available make it unlikely that a new
submodule will be added in the near future.
There is also
optimize, which is still private
but could form a solid basis for this module.
This module was discussed previously, mainly to provide a home for discrete wavelet transform functionality. Other transforms could fit as well, for example there’s a PR for a Hankel transform . Note: this is on the back burner, because the plans to integrate PyWavelets DWT code has been put on hold.