Detailed SciPy Roadmap#

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.

Note

This is the detailed roadmap. A very high-level overview with only the most important ideas is SciPy Roadmap.

General#

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 forum.

API changes#

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.

Test coverage#

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 (stats, signal and ndimage in particular) is necessary.

Documentation#

The documentation is in good shape. Expanding of current docstrings - adding examples, references, and better explanations - should continue. 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.

Benchmarks#

The asv-based benchmark system is in reasonable shape. It is quite easy to add new benchmarks, however running the benchmarks is not very intuitive. Making this easier is a priority.

Use of Cython#

Cython’s old syntax for using NumPy arrays should be removed and replaced with Cython memoryviews.

Binary sizes of extensions built from Cython code are large, and compile times are long. We should aim to combine extension modules where possible (e.g., stats._boost contains many extension modules now), and limit the use of Cython to places where it’s the best choice. Note that conversion of Cython to C++ is ongoing in scipy.special.

Use of Pythran#

Pythran is still an optional build dependency, and can be disabled with -Duse-pythran=false. The aim is to make it a hard dependency - for that to happen it must be clear that the maintenance burden is low enough.

Use of venerable Fortran libraries#

SciPy owes a lot of its success to relying on wrapping well established Fortran libraries (QUADPACK, FITPACK, ODRPACK, ODEPACK etc). Some of these libraries are aging well, others less so. We should audit our use of these libraries with respect to the maintenance effort, the functionality, and the existence of (possibly partial) alternatives, including those inside SciPy.

Continuous integration#

Continuous integration currently covers 32/64-bit Windows, macOS on x86-64/arm, 32/64-bit Linux on x86, and Linux on aarch64 - as well as a range of versions of our dependencies and building release quality wheels. Reliability of CI has not been good recently (H1 2023), due to the large amount of configurations to support and some CI jobs needing an overhaul. We aim to reduce build times by removing the remaining distutils-based jobs when we drop that build system and make the set of configurations in CI jobs more orthogonal.

Size of binaries#

SciPy binaries are quite large (e.g. an unzipped manylinux wheel for 1.7.3 is 39 MB on PyPI and 122 MB after installation), and this can be problematic - for example for use in AWS Lambda, which has a 250 MB size limit. We aim to keep binary size as low as possible; when adding new compiled extensions, this needs checking. Stripping of debug symbols in multibuild can perhaps be improved (see this issue). An effort should be made to slim down where possible, and not add new large files. In the future, things that are being considered (very tentatively) and may help are separating out the bundled` libopenblas and removing support for long double.

Modules#

cluster#

dendrogram needs a rewrite, it has a number of hard to fix open issues and feature requests.

constants#

This module is basically done, low-maintenance and without open issues.

fft#

This module is in good shape.

integrate#

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.

interpolate#

Spline fitting: we need spline fitting routines with better user control. This includes

  • user-selectable alternatives for the smoothing criteria (manual, cross-validation etc); gh-16653 makes a start in this direction;

  • several strategies for knot placement, both manual and automatic (using algorithms by Dierckx, de Boor, possibly other).

Once we have a reasonably feature complete set, we can start taking a long look at the future of the venerable FITPACK Fortran library, which currently is the only way of constructing smoothing splines in SciPy.

Scalability and performance: For the FITPACK-based functionality, the data size is limited by 32-bit Fortran integer size (for non-ILP64 builds). For N-D scattered interpolators (which are QHull based) and N-D regular grid interpolators we need to check performance on large data sets and improve where lacking (gh-16483 makes progress in this direction).

Ideas for new features: NURBS support could be added.

io#

wavfile:

  • PCM float will be supported, for anything else use audiolab or 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.

linalg#

scipy.linalg is in good shape.

Needed:

  • Reduce duplication of functions with numpy.linalg, make APIs consistent.

  • get_lapack_funcs should always use flapack

  • 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

BLAS and LAPACK

The Python and Cython interfaces to BLAS and LAPACK in scipy.linalg are one of the most important things that SciPy provides. In general scipy.linalg is in good shape, however we can make a number of improvements:

  1. Library support. Our released wheels now ship with OpenBLAS, which is currently the only feasible performant option (ATLAS is too slow, MKL cannot be the default due to licensing issues, Accelerate support is dropped because Apple doesn’t update Accelerate anymore). OpenBLAS isn’t very stable though, sometimes its releases break things and it has issues with threading (currently the only issue for using SciPy with PyPy3). We need at the very least better support for debugging OpenBLAS issues, and better documentation on how to build SciPy with it. An option is to use BLIS for a BLAS interface (see numpy gh-7372).

  2. Support for newer LAPACK features. In SciPy 1.2.0 we increased the minimum supported version of LAPACK to 3.4.0. Now that we dropped Python 2.7, we can increase that version further (MKL + Python 2.7 was the blocker for >3.4.0 previously) and start adding support for new features in LAPACK.

misc#

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:

  • derivative, central_diff_weight : remove, possibly replacing them with more extensive functionality for numerical differentiation.

  • ascent, face, electrocardiogram : remove or move to the appropriate subpackages (e.g. scipy.ndimage, scipy.signal).

ndimage#

Underlying ndimage is a powerful interpolation engine. Users come with an expectation of one of two models: a pixel model with (1, 1) elements having centers (0.5, 0.5), or a data point model, where values are defined at points on a grid. Over time, we’ve become convinced that the data point model is better defined and easier to implement, but this should be clearly communicated in the documentation.

More importantly, still, SciPy implements one variant of this data point model, where datapoints at any two extremes of an axis share a spatial location under periodic wrapping mode. E.g., in a 1D array, you would have x[0] and x[-1] co-located. A very common use-case, however, is for signals to be periodic, with equal spacing between the first and last element along an axis (instead of zero spacing). Wrapping modes for this use-case were added in gh-8537, next the interpolation routines should be updated to use those modes. This should address several issues, including gh-1323, gh-1903, gh-2045 and gh-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.

odr#

This module is in reasonable shape, although it could use a bit more maintenance. No major plans or wishes here.

optimize#

Overall this module is in good shape. Two good global optimizers were added in 1.2.0; large-scale optimizers is still a gap that could be filled. Other things that are needed:

  • Many ideas for additional functionality (e.g. integer constraints) in linprog, see gh-9269.

  • Add functionality to the benchmark suite to compare results more easily (e.g. with summary plots).

  • deprecate the fmin_* functions in the documentation, minimize is preferred.

  • scipy.optimize has an extensive set of benchmarks for accuracy and speed of the global optimizers. That has allowed adding new optimizers (shgo and dual_annealing) with significantly better performance than the existing ones. The optimize benchmark system itself is slow and hard to use however; we need to make it faster and make it easier to compare performance of optimizers via plotting performance profiles.

signal#

Convolution and correlation: (Relevant functions are convolve, correlate, fftconvolve, convolve2d, correlate2d, and sepfir2d.) Eliminate the overlap with ndimage (and elsewhere). From numpy, scipy.signal and scipy.ndimage (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 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: 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.

sparse#

The sparse matrix formats are mostly feature-complete, however the main issue is that they act like numpy.matrix (which will be deprecated in NumPy at some point).

What we want is sparse arrays that act like numpy.ndarray. Initial support for a new set of classes (csr_array et al.) was added in SciPy 1.8.0 and stabilized in 1.12.0 when construction functions for arrays were added. Support for 1-D array is expected in 1.13.0.

Next steps toward sparse array support:

  • Extend sparse array API to 1-D arrays.

    • Support for COO, CSR and DOK formats.

    • CSR 1D support for min-max, indexing, arithmetic.

  • Help other libraries convert to sparse arrays from sparse matrices. Create transition guide and helpful scripts to flag code that needs further examination. NetworkX, scikit-learn and scikit-image are in progress or have completed conversion to sparse arrays.

  • After sparse array code is mature (~1 release cycle?) add deprecation warnings for sparse matrix.

  • Work with NumPy on deprecation/removal of numpy.matrix.

  • Deprecate and then remove sparse matrix in favor of sparse array.

  • Start API shift of construction function names (diags, block, etc.)

    • Note: as a whole, the construction functions undergo two name shifts. Once to move from matrix creation to new functions for array creation (i.e. eye -> eye_array). Then a second move to change names to match the array_api name (i.e. eye_array to eye) after sparse matrices are removed. We will keep the *_array versions for a long time as (maybe hidden) aliases.

  • Add construction function names matching array_api names.

  • Deprecate the transition construction function names.

An alternative (more ambitious, and unclear if it will materialize) plan is being worked on in pydata/sparse. To support that effort we aim to support PyData Sparse in all functions that accept sparse arrays. Support for pydata/sparse in scipy.sparse.linalg and scipy.sparse.csgraph is mostly complete.

Regarding the different sparse matrix formats: there are a lot of them. 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.

sparse.csgraph#

This module is in good shape.

sparse.linalg#

There are a significant number of open issues for _arpack and lobpcg. _propack is new in 1.8.0, TBD how robust it will turn out to be.

_isolve:

  • callback keyword is inconsistent

  • tol keyword is broken, should be relative tol

  • Fortran code not re-entrant (but we don’t solve, maybe reuse from PyKrilov)

_dsolve:

  • add license-compatible sparse Cholesky or incomplete Cholesky

  • add license-compatible sparse QR

  • improve interface to SuiteSparse UMFPACK

  • add interfaces to SuiteSparse CHOLMOD and SPQR

spatial#

QHull wrappers are in good shape, as is KDTree.

A rewrite of spatial.distance metrics in C++ is in progress - this should improve performance, make behaviour (e.g., for various non-float64 input dtypes) more consistent, and fix a few remaining issues with definitions of the math implement by a few of the metrics.

special#

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:

  1. 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.

  2. 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: radelman/scattering.

  3. Add clear warnings to the documentation about the limits of the existing implementations.

stats#

The scipy.stats subpackage aims to provide fundamental statistical methods as might be covered in standard statistics texts such as Johnson’s “Miller & Freund’s Probability and Statistics for Engineers”, Sokal & Rohlf’s “Biometry”, or Zar’s “Biostatistical Analysis”. It does not seek to duplicate the advanced functionality of downstream packages (e.g. StatsModels, LinearModels, PyMC3); instead, it can provide a solid foundation on which they can build. (Note that these are rough guidelines, not strict rules. “Advanced” is an ill-defined and subjective term, and “advanced” methods may also be included in SciPy, especially if no other widely used and well-supported package covers the topic. Also note that some duplication with downstream projects is inevitable and not necessarily a bad thing.)

In addition to the items described in the SciPy Roadmap, the following improvements will help SciPy better serve this role.

  • Add fundamental and widely used hypothesis tests, such as:

    • post hoc tests (e.g. Dunnett’s test)

    • the various types of analysis of variance (ANOVA):

      • two-way ANOVA (single replicate, uniform number of replicates, variable number of replicates)

      • multiway ANOVA (i.e. generalize two-way ANOVA)

      • nested ANOVA

      • analysis of covariance (ANCOVA)

    Also, provide an infrastructure for implementing hypothesis tests.

  • Add additional tools for meta-analysis

  • Add tools for survival analysis

  • Speed up random variate sampling (method rvs) of distributions, leveraging scipy.stats.sampling where appropriate

  • Expand QMC capabilities and performance

  • Enhance the fit method of the continuous probability distributions:

    • Expand the options for fitting to include:

      • maximal product spacings

      • method of L-moments / probability weighted moments

    • Include measures of goodness-of-fit in the results

    • Handle censored data (e.g. merge gh-13699)

  • Implement additional widely used continuous and discrete probability distributions, e.g. mixture distributions.

  • Improve the core calculations provided by SciPy’s probability distributions so they can robustly handle wide ranges of parameter values. Specifically, replace many of the PDF and CDF methods from the Fortran library CDFLIB used in scipy.special with Boost implementations as in gh-13328.

In addition, we should:

  • Continue work on making the function signatures of stats and stats.mstats more consistent, and add tests to ensure that that remains the case.

  • Improve statistical tests: return confidence intervals for the test statistic, and implement exact p-value calculations - considering the possibility of ties - where computationally feasible.