Search
Searching.
- Using Python as glue
...NumPy user guide Using NumPy C-API Using Python as glue...
numpy.distutilsuser guide...numpy.distutils user guide Warning numpy.distutils is deprecated, and will be removed for Python >= 3.12. For more details, see Status of numpy.distutils and migration advice SciPy structure Currently SciPy p...
- Advanced debugging tools
...ange is released and means you do not have to worry about making reference counting errors, which can be intimidating. Python debug build Debug builds of Python are easily available for example via the system package manager on Linux syste...
- Advanced F2PY use cases
...Advanced F2PY use cases Adding user-defined functions to F2PY generated modules User-defined Python C/API functions can be defined inside signature files using usercode and pymethoddef statements (they must be use...
- Array API
...s the memory location item of dtype descr to value. The function is equivalent to setting a single array element with a Python assignment. Returns 0 on success and -1 with an error set on failure. Note If the descr has the NPY_NEEDS_INIT...
- Array creation
...also Array creation routines Introduction There are 6 general mechanisms for creating arrays: Conversion from other Python structures (i.e. lists and tuples) Intrinsic NumPy array creation functions (e.g. arange, ones, zeros, etc.) Repl...
- BLAS and LAPACK
...er, and which libraries are tried, can be changed through the blas-order and lapack-order build options, for example: $ python -m pip install . -Csetup-args=-Dblas-order=openblas,mkl,blis -Csetup-args=-Dlapack-order=openblas,mkl,lapack Th...
- Boilerplate reduction and templating
...ne via f2py -m adder adder_base.f90 -h adder.pyf: ! -*- f90 -*- ! Note: the context of this file is case sensitive. python module adder ! in interface ! in :adder module adder ! in :adder:adder_base.f90 subrouti...
- Building from source
...n addition, it provides background information on how the NumPy build works, and links to up-to-date guides for generic Python build & packaging documentation that is relevant. System-level dependencies NumPy uses compiled code for speed,...
- Compiler selection and customizing a build
...e that happens, lower the number of jobs N such that you have at least 2 GB RAM per job. For example, to launch 6 jobs: python -m pip install . -Ccompile-args="-j6" or: spin build -j6...
- CPU build options
...g the runtime, NumPy modules will fail to load if any of specified features are not supported by the target CPU (raises Python runtime error). cpu-dispatch: dispatched set of additional CPU features.Default value is max -xop -fma4 whic...
- Data type promotion in NumPy
...ith non-default dtypes like unsigned integers and low-precision floats, or if you mix NumPy integers, NumPy floats, and Python scalars, some details of NumPy promotion rules may be relevant. Note that these detailed rules do not always matc...
- F2PY and Windows
...ack of actively developed FOSS Fortran compilers, and, the linking issues related to the C runtime library for building Python-C extensions. The focus of this section is to establish a guideline for developing and extending Fortran modules...
- Global Configuration Options
...mPy itself is normally intentionally limited to a single thread during function calls, however it does support multiple Python threads running at the same time. Note that for performant linear algebra NumPy uses a BLAS backend such as OpenB...
- Importing data with
genfromtxt...import. Remember that by convention, the first column has an index of 0. Negative integers behave the same as regular Python negative indexes. For example, if we want to import only the first and the last columns, we can use usecols=(0, -...
- Interoperability with NumPy
...ible. When NumPy functions encounter a foreign object, they will try (in order): The buffer protocol, described in the Python C-API documentation. The __array_interface__ protocol, described in this page. A precursor to Python’s buffer pro...
- Iterating over arrays
...Iterating over arrays Note Arrays support the iterator protocol and can be iterated over like Python lists. See the Indexing, slicing and iterating section in the Quickstart guide for basic usage and examples. The...
- NumPy 1.10.0 Release Notes
...NumPy 1.10.0 Release Notes This release supports Python 2.6 - 2.7 and 3.2 - 3.5. Highlights numpy.distutils now supports parallel compilation via the –parallel/-j argu...
- NumPy 1.11.0 Release Notes
...NumPy 1.11.0 Release Notes This release supports Python 2.6 - 2.7 and 3.2 - 3.5 and contains a number of enhancements and improvements. Note also the build system change...
- NumPy 1.13.0 Release Notes
...NumPy 1.13.0 Release Notes This release supports Python 2.7 and 3.4 - 3.6. Highlights Operations like a + b + c will reuse temporaries on some platforms, resulting in...
- NumPy 1.14.0 Release Notes
...ve the old style printing when needed. A major decision affecting future development concerns the schedule for dropping Python 2.7 support in the runup to 2020. The decision has been made to support 2.7 for all releases made in 2018, with t...
- NumPy 1.15.0 Release Notes
...maintained nose framework. The old nose based interface remains for downstream projects who may still be using it. The Python versions supported by this release are 2.7, 3.4-3.7. The wheels are linked with OpenBLAS v0.3.0, which should fix...
- NumPy 1.16.2 Release Notes
...NumPy 1.16.2 Release Notes NumPy 1.16.2 is a quick release fixing several problems encountered on Windows. The Python versions supported are 2.7 and 3.5-3.7. The Windows problems addressed are: DLL load problems for NumPy wheels o...
- NumPy 1.17.0 Release Notes
- NumPy 1.19.0 Release Notes
- NumPy 1.20.0 Release Notes
- NumPy 1.21.0 Release Notes
- NumPy 1.22.0 Release Notes
- NumPy 1.23.0 Release Notes
- NumPy 1.24 Release Notes
- NumPy 1.25.0 Release Notes
- NumPy 1.3.0 Release Notes
- NumPy 1.4.0 Release Notes
- NumPy 1.5.0 Release Notes
- NumPy 1.8.0 Release Notes
- NumPy 1.9.0 Release Notes
- NumPy 2.0.0 Release Notes
- NumPy core math library
- NumPy for MATLAB users
- NumPy reference
- NumPy security
- numpy.i: a SWIG interface file for NumPy
- NumPy: the absolute basics for beginners
- Python types and C-structures
- Reading and writing files
- Releasing a version
- Setting up and using your development environment
- Signature file
- Structured arrays
- Subclassing ndarray
- Testing guidelines
- Testing the numpy.i typemaps
- The array interface protocol
- Thread Safety
- Troubleshooting
- Universal functions (
ufunc) basics - Using F2PY
- Using F2PY bindings in Python
- Using NumPy C-API
- Using Python as glue
- Using the convenience classes
- Using via
cmake - Using via
meson - Using via
scikit-build - Using via
numpy.distutils - What is NumPy?
- 1 Migrating to
meson - Array API standard compatibility
- Array iterator API
- Array objects
- Beyond the basics
- Bit generators
- Broadcasting
- Building redistributable binaries
- Building the NumPy API and reference docs
- Byte-swapping
- C API deprecations
- Contributing to NumPy
- Cross compilation
- Data type API
- Data type objects (
dtype) - Data types
- Datetime API
- Datetimes and timedeltas
- Development workflow
- Extending
- F2PY and build systems
- F2PY and Windows Intel Fortran