Toolchain Roadmap#

The use of the SciPy library requires (or optionally depends upon) several other libraries in order to operate, the main dependencies being Python and NumPy. It requires a larger collection of libraries and tools in order to build the library or to build the documentation.

Of course, the tooling and libraries are themselves not static. This document aims to provide a guide as to how SciPy’s use of these dynamic dependencies will proceed over time.

SciPy aims to be compatible with a number of releases of its dependent libraries and tools. Forcing the user base to other components for upgrade for every release would greatly diminish the value of SciPy. However, maintaining backwards compatibility with very old tooling/libraries imposes limitations on which newer functionalities and capabilities can be incorporated. SciPy takes a somewhat conservative approach, maintaining compatibility with several major releases of Python and NumPy on the major platforms. (That may in and of itself impose further restrictions. See the C Compilers section for an example.)

First and foremost, SciPy is a Python project, hence it requires a Python environment.
BLAS and LAPACK numerical libraries need to be installed.
Compilers for C, C++, Fortran code are needed, as well as for Cython & Pythran (the latter is opt-out currently)
The Python environment needs the NumPy package to be installed.
Testing requires the pytest Python package.
Building the documentation requires the matplotlib, Sphinx packages along with PyData theme, as well as a LaTeX installation.

The tooling used to build CPython has some implications for the tooling used in building SciPy. It also has implications for the examples used in the documentation (e.g., docstrings for functions), as these examples can only use functionality present in all supported configurations.

Building SciPy#

Python Versions#

SciPy is compatible with several versions of Python. When dropping support for older Python versions, SciPy takes guidance from NEP 29 1. Python 2.7 support was dropped starting from SciPy 1.3.

Date	Pythons supported
2018	Py2.7, Py3.4+ (SciPy 1.2.x is the last release to support Python 2.7)
2019	Py3.5+ (but Py2.7-specific code not removed)
2020	Py3.6+ (removal of Py2.7-specific code permitted)
2021	Py3.7+
2022	Py3.8+

NumPy#

SciPy depends on NumPy but releases of SciPy are not tied to releases of NumPy. SciPy attempts to be compatible with at least the 4 previous releases of NumPy. In particular, SciPy cannot rely on features of just the latest NumPy, but needs to be written using what is common in all of those 4 releases 2.

The table shows the NumPy versions suitable for each major Python version.

SciPy version	Python versions	NumPy versions
1.2	2.7, >=3.4, <=3.7	>=1.8.2, <= 1.16.x
1.4	>=3.5, <=3.8	>=1.13.3, <= 1.17.3
1.5	>=3.6, <=3.9	>=1.14.5, <= 1.19.3
1.6	>=3.7, <=3.9	>=1.16.5, <= 1.20.x
1.7.0/1	>=3.7, <3.10	>=1.16.5, <1.23.0
1.7.2-x	>=3.7, <3.11	>=1.16.5, <1.24.0
1.8	>=3.8, <3.11	>=1.17.3, <1.24.0

In specific cases, such as a particular architecture, these requirements could vary. Please check the release notes 3 and the meta-package oldest-supported-numpy for more info 4.

Compilers#

Building SciPy requires compilers for C, C++, Fortran, as well as the python transpilers Cython and Pythran (the latter is an opt-out dependency starting from version 1.7.0).

To maintain compatibility with a large number of platforms & setups, especially where using the official wheels (or other distribution channels like Anaconda or conda-forge) is not possible, SciPy keeps compatibility with old compilers.

Official Builds#

Currently, SciPy wheels are being built as follows:

Platform	Azure Base Image 5	Compilers	Comment
Linux (nightly)	`ubuntu-18.04`	GCC 6.5	See `azure-pipelines.yml`
Linux (release)	`ubuntu-18.04`	GCC 7.5	Built in separate repo 6
OSX	`macOS-10.15`	LLVM 12.0.0	Built in separate repo 6
Windows	`windows-2019`	Visual Studio 2019 (16.11)	See `azure-pipelines.yml`

Note that the OSX wheels additionally vendor gfortran 4.9, see submodule gfortran-install in 6.

C Compilers#

SciPy is compatible with most modern C compilers (in particular clang). In addition to concerns about compatibility with non-standard platforms, there was a long-standing restriction that Windows builds of SciPy had to use the same version of the Microsoft Visual C++ compiler as were used for CPython itself, for reasons of ABI-compatibility 7, 8.

With the introduction of the “Universal C Runtime” 9 since the release of Visual Studio 2015, this restriction has been lifted. For more context, see the explanations by Steve Dower (member of the CPython-on-Windows core developers) on this topic 10.

The use of MS Visual Studio 9.0 (which doesn’t have support for C99) to build Python 2.7 has meant that C code in SciPy has had to conform to the earlier C90 standard for the language and standard library. With the dropping of Python 2.7 for SciPy 1.3.x, the C90 restriction is no longer imposed by compilers.

In terms of C language standards, it’s relevant to note that C11 has optional features 11 (e.g. atomics, threading), some of which (VLAs & complex types) were mandatory in the C99 standard. C17 (occasionally called C18) can be considered a bug fix for C11, so generally, C11 may be skipped entirely.

SciPy has been restricted in the use of more advanced language features by the available compiler support, and Microsoft in particular has taken very long to achieve conformance to C99/C11/C17, however starting from MS Visual Studio 16.8, C11/C17 is supported 12 (though without the C11 optional features). C99 <complex.h> would be particularly interesting for SciPy; MSVC conformance for this is being tracked here 13.

Therefore, using C features beyond C90 was only possible insofar there was support on windows; however, as of as of the end of 2021, a sufficiently recent compiler is used. This is because GCC & LLVM support all relevant C11 features with the oldest currently used versions, and C17 is just a bugfix for C11, as mentioned above. In short:

Date	C Standard
<= 2018	C90
2019	C90 for old code, may consider C99 for new
2020	C99 (no `<complex.h>`, `<stdatomic.h>`, `<threads.h>` & VLAs)
2021	C17 (no `<complex.h>`, `<stdatomic.h>`, `<threads.h>` & VLAs)
?	C23, `<complex.h>`, `<stdatomic.h>`, …

C++ Language Standards#

C++ language standards for SciPy are generally guidelines rather than official decisions. This is particularly true of attempting to predict adoption timelines for newer standards.

Date	C++ Standard
<= 2019	C++03
2020	C++11
2021	C++14
?	C++17, C++20, C++23

Since dropping support for Python 2.7, C++11 can be used universally, and since dropping Python 3.6, the Visual Studio version (that had previously been stuck with 14.0 due to ABI compatibility with CPython) has been recent enough to support even C++17.

Since the official builds (see above) use a pretty recent version of LLVM, the bottleneck for C++ support is therefore the oldest supported GCC version, where SciPy has been constrained mainly by the version in the oldest supported manylinux versions & images 14.

At the end of 2021 (with the final removal of manylinux1 wheels), SciPy now has a minimum GCC requirement of GCC 6.3, which has full C++14 support 15. This corresponds to the lowest present GCC version in relevant manylinux versions - somewhat surprisingly, it is not the oldest remaining manylinux2010 that is the most restrictive (due to the ABI-compatible “RHEL Dev Toolset” backports, it has GCC 8.3), but actually manylinux_2_24 that only comes with GCC 6.3 16.

C++17 _language_ support will require GCC >= 7 (released May 2017). As of the end of 2021, support for the entirety of the C++17 standard library has not yet been completed across all compilers; similarly, support for C++20 and C++23 is still under heavy development. 15

Fortran Compilers#

Generally, any well-maintained compiler is likely suitable and can be used to build SciPy.

Tool	Version
gfortran	>= 4.8.0
ifort	A recent version
flang	A recent version

Cython & Pythran#

SciPy always requires a recent Cython compiler. Since 1.7, Pythran is a build dependency (currently with the possibility to opt out).

OpenMP support#

For various reasons 17, SciPy cannot be distributed with built-in OpenMP support. When using the optional Pythran support, OpenMP-enabled parallel code can be generated when building from source.

Other Libraries#

Any library conforming to the BLAS/LAPACK interface may be used. OpenBLAS, ATLAS, MKL, BLIS, and reference Netlib libraries are known to work.

Library	Minimum version
LAPACK	3.4.1
BLAS	A recent version of OpenBLAS, MKL or ATLAS. The Accelerate BLAS library is no longer supported.

There are some additional optional dependencies.

Library	Version	URL
mpmath	Recent	http://mpmath.org/
scikit-umfpack	Recent	https://pypi.org/project/scikit-umfpack/

Moreover, Scipy supports interaction with other libraries. The test suite has additional compatibility tests that are run when these are installed:

Tool	Version	URL
pydata/sparse	Recent	https://github.com/pydata/sparse/

Testing and Benchmarking#

Testing and benchmarking require recent versions of:

Tool	Version	URL
pytest	Recent	https://docs.pytest.org/en/latest/
asv (airspeed velocity)	Recent	https://asv.readthedocs.io/

Building the Documentation#

Tool	Version
Sphinx	Whatever recent versions work. >= 2.0.
PyData Sphinx theme	Whatever recent versions work. >= 0.6.1.
Sphinx-Panels	Whatever recent versions work. >= 0.5.2.
numpydoc	Whatever recent versions work. >= 0.8.0.
matplotlib	Generally suggest >= 2.0.
LaTeX	A recent distribution, such as `TeX Live 2016`.

[The numpydoc package is also used, but that is currently packaged in doc/sphinxext.]

Note

Developer Note: The versions of numpy and matplotlib required have implications for the examples in Python docstrings. Examples must be able to be executed both in the environment used to build the documentation, as well as with any supported versions of numpy/matplotlib that a user may use with this release of SciPy.

Packaging#

A Recent version of:

Tool	Version	URL
setuptools	Recent	https://pypi.org/project/setuptools/
wheel	Recent	https://pythonwheels.com
multibuild	Recent	https://github.com/matthew-brett/multibuild

Making a SciPy release and Distributing contain information on making and distributing a SciPy release.

References#

1: https://numpy.org/neps/nep-0029-deprecation_policy.html
2: https://numpy.org/doc/stable/release.html
3: https://scipy.github.io/devdocs/release.html
4: https://github.com/scipy/oldest-supported-numpy
5: https://docs.microsoft.com/en-us/azure/devops/pipelines/agents/hosted
6(1,2,3): https://github.com/MacPython/scipy-wheels
7: https://pythondev.readthedocs.io/windows.html#python-and-visual-studio-version-matrix
8: https://en.wikipedia.org/wiki/Microsoft_Visual_C%2B%2B#Internal_version_numbering
9: https://docs.microsoft.com/en-gb/cpp/windows/universal-crt-deployment
10: https://discuss.python.org/t/toolchain-upgrade-on-windows/6377/4
11: https://en.wikipedia.org/wiki/C11_%28C_standard_revision%29#Optional_features
12: https://devblogs.microsoft.com/cppblog/c11-and-c17-standard-support-arriving-in-msvc/
13: https://developercommunity.visualstudio.com/t/Support-for-C99-Complex-numbers/1409049?space=8&q=complex
14: https://github.com/mayeut/pep600_compliance
15(1,2): https://en.cppreference.com/w/cpp/compiler_support
16: https://github.com/pypa/manylinux/issues/1012
17: https://github.com/scipy/scipy/issues/10239

Detailed SciPy Roadmap

SciPy Core Developer Guide