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public_repos/numpy/doc/source/reference | public_repos/numpy/doc/source/reference/distutils/misc_util.rst | distutils.misc_util
===================
.. automodule:: numpy.distutils.misc_util
:members:
:undoc-members:
:exclude-members: Configuration
| 0 |
public_repos/numpy/doc/source/reference | public_repos/numpy/doc/source/reference/simd/build-options.rst | *****************
CPU build options
*****************
Description
-----------
The following options are mainly used to change the default behavior of optimizations
that target certain CPU features:
- ``--cpu-baseline``: minimal set of required CPU features.
Default value is ``min`` which provides the minimum CPU ... | 0 |
public_repos/numpy/doc/source/reference | public_repos/numpy/doc/source/reference/simd/gen_features.py | """
Generate CPU features tables from CCompilerOpt
"""
from os import sys, path
from numpy.distutils.ccompiler_opt import CCompilerOpt
class FakeCCompilerOpt(CCompilerOpt):
# disable caching no need for it
conf_nocache = True
def __init__(self, arch, cc, *args, **kwargs):
self.fake_info = (arch, c... | 0 |
public_repos/numpy/doc/source/reference | public_repos/numpy/doc/source/reference/simd/log_example.txt | ########### EXT COMPILER OPTIMIZATION ###########
Platform :
Architecture: x64
Compiler : gcc
CPU baseline :
Requested : 'min'
Enabled : SSE SSE2 SSE3
Flags : -msse -msse2 -msse3
Extra checks: none
CPU dispatch :
Requested : 'max -xop -fma4'
Enabled : SSSE3 SSE41 POPCNT SSE... | 0 |
public_repos/numpy/doc/source/reference | public_repos/numpy/doc/source/reference/simd/how-it-works.rst | *********************************
How does the CPU dispatcher work?
*********************************
NumPy dispatcher is based on multi-source compiling, which means taking
a certain source and compiling it multiple times with different compiler
flags and also with different **C** definitions that affect the code
pat... | 0 |
public_repos/numpy/doc/source/reference | public_repos/numpy/doc/source/reference/simd/index.rst | .. _numpysimd:
.. currentmodule:: numpysimd
***********************
CPU/SIMD optimizations
***********************
NumPy comes with a flexible working mechanism that allows it to harness the SIMD
features that CPUs own, in order to provide faster and more stable performance
on all popular platforms. Currently, NumPy ... | 0 |
public_repos/numpy/doc/source/reference | public_repos/numpy/doc/source/reference/simd/simd-optimizations.rst | :orphan:
.. raw:: html
<html>
<head>
<meta http-equiv="refresh" content="0; url=index.html"/>
</head>
</html>
The location of this document has been changed , if you are not
redirected in few seconds, `click here <index.html>`_.
| 0 |
public_repos/numpy/doc/source/reference/simd | public_repos/numpy/doc/source/reference/simd/generated_tables/compilers-diff.inc | .. generated via /numpy/numpy/./doc/source/reference/simd/gen_features.py
On x86::Intel Compiler
~~~~~~~~~~~~~~~~~~~~~~
.. table::
:align: left
====================== ===============================================================================================================================================... | 0 |
public_repos/numpy/doc/source/reference/simd | public_repos/numpy/doc/source/reference/simd/generated_tables/cpu_features.inc | .. generated via /numpy/numpy/./doc/source/reference/simd/gen_features.py
On x86
~~~~~~
.. table::
:align: left
============== =======================================================================================================================================================================================... | 0 |
public_repos/numpy/doc/source/reference | public_repos/numpy/doc/source/reference/c-api/dtype.rst | Data type API
=============
.. sectionauthor:: Travis E. Oliphant
The standard array can have 24 different data types (and has some
support for adding your own types). These data types all have an
enumerated type, an enumerated type-character, and a corresponding
array scalar Python type object (placed in a hierarchy... | 0 |
public_repos/numpy/doc/source/reference | public_repos/numpy/doc/source/reference/c-api/iterator.rst | Array iterator API
==================
.. sectionauthor:: Mark Wiebe
.. index::
pair: iterator; C-API
pair: C-API; iterator
.. versionadded:: 1.6
Array iterator
--------------
The array iterator encapsulates many of the key features in ufuncs,
allowing user code to support features like output parameters,
pre... | 0 |
public_repos/numpy/doc/source/reference | public_repos/numpy/doc/source/reference/c-api/data_memory.rst | .. _data_memory:
Memory management in NumPy
==========================
The `numpy.ndarray` is a python class. It requires additional memory allocations
to hold `numpy.ndarray.strides`, `numpy.ndarray.shape` and
`numpy.ndarray.data` attributes. These attributes are specially allocated
after creating the python object ... | 0 |
public_repos/numpy/doc/source/reference | public_repos/numpy/doc/source/reference/c-api/deprecations.rst | .. _c_api_deprecations:
C API deprecations
==================
Background
----------
The API exposed by NumPy for third-party extensions has grown over
years of releases, and has allowed programmers to directly access
NumPy functionality from C. This API can be best described as
"organic". It has emerged from multi... | 0 |
public_repos/numpy/doc/source/reference | public_repos/numpy/doc/source/reference/c-api/array.rst | Array API
=========
.. sectionauthor:: Travis E. Oliphant
| The test of a first-rate intelligence is the ability to hold two
| opposed ideas in the mind at the same time, and still retain the
| ability to function.
| --- *F. Scott Fitzgerald*
| For a successful technology, reality must take precedence... | 0 |
public_repos/numpy/doc/source/reference | public_repos/numpy/doc/source/reference/c-api/datetimes.rst | Datetime API
============
NumPy represents dates internally using an int64 counter and a unit metadata
struct. Time differences are represented similarly using an int64 and a unit
metadata struct. The functions described below are available to to facilitate
converting between ISO 8601 date strings, NumPy datetimes, an... | 0 |
public_repos/numpy/doc/source/reference | public_repos/numpy/doc/source/reference/c-api/index.rst | .. _c-api:
###########
NumPy C-API
###########
.. sectionauthor:: Travis E. Oliphant
| Beware of the man who won't be bothered with details.
| --- *William Feather, Sr.*
| The truth is out there.
| --- *Chris Carter, The X Files*
NumPy provides a C-API to enable users to extend the system and get
acce... | 0 |
public_repos/numpy/doc/source/reference | public_repos/numpy/doc/source/reference/c-api/types-and-structures.rst |
*****************************
Python types and C-structures
*****************************
.. sectionauthor:: Travis E. Oliphant
Several new types are defined in the C-code. Most of these are
accessible from Python, but a few are not exposed due to their limited
use. Every new Python type has an associated :c:expr:`P... | 0 |
public_repos/numpy/doc/source/reference | public_repos/numpy/doc/source/reference/c-api/coremath.rst | NumPy core libraries
====================
.. sectionauthor:: David Cournapeau
Starting from numpy 1.3.0, we are working on separating the pure C,
"computational" code from the python dependent code. The goal is twofolds:
making the code cleaner, and enabling code reuse by other extensions outside
numpy (scipy, etc...... | 0 |
public_repos/numpy/doc/source/reference | public_repos/numpy/doc/source/reference/c-api/ufunc.rst | ufunc API
=========
.. sectionauthor:: Travis E. Oliphant
.. index::
pair: ufunc; C-API
Constants
---------
``UFUNC_{THING}_{ERR}``
.. c:macro:: UFUNC_FPE_DIVIDEBYZERO
.. c:macro:: UFUNC_FPE_OVERFLOW
.. c:macro:: UFUNC_FPE_UNDERFLOW
.. c:macro:: UFUNC_FPE_INVALID
``PyUFunc_{VALUE}``
.. c... | 0 |
public_repos/numpy/doc/source/reference | public_repos/numpy/doc/source/reference/c-api/config.rst | System configuration
====================
.. sectionauthor:: Travis E. Oliphant
When NumPy is built, information about system configuration is
recorded, and is made available for extension modules using NumPy's C
API. These are mostly defined in ``numpyconfig.h`` (included in
``ndarrayobject.h``). The public symbols... | 0 |
public_repos/numpy/doc/source/reference | public_repos/numpy/doc/source/reference/c-api/generalized-ufuncs.rst | .. _c-api.generalized-ufuncs:
==================================
Generalized universal function API
==================================
There is a general need for looping over not only functions on scalars
but also over functions on vectors (or arrays).
This concept is realized in NumPy by generalizing the universal ... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/dev/reviewer_guidelines.rst | .. _reviewer-guidelines:
===================
Reviewer guidelines
===================
Reviewing open pull requests (PRs) helps move the project forward. We encourage
people outside the project to get involved as well; it's a great way to get
familiar with the codebase.
Who can be a reviewer?
======================
R... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/dev/development_advanced_debugging.rst | .. _advanced_debugging:
========================
Advanced debugging tools
========================
If you reached here, you want to dive into, or use, more advanced tooling.
This is usually not necessary for first time contributors and most
day-to-day development.
These are used more rarely, for example close to a ne... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/dev/howto-docs.rst | .. _howto-docs:
############################################
How to contribute to the NumPy documentation
############################################
This guide will help you decide what to contribute and how to submit it to the
official NumPy documentation.
***************************
Documentation team meetings
*... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/dev/releasing.rst | ===================
Releasing a version
===================
The following guides include detailed information on how to prepare a NumPy
release.
.. _prepare_release:
------------------------
How to prepare a release
------------------------
.. include:: ../../HOWTO_RELEASE.rst
-----------------------
Step-by-step... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/dev/alignment.rst | .. currentmodule:: numpy
.. _alignment:
****************
Memory alignment
****************
NumPy alignment goals
=====================
There are three use-cases related to memory alignment in NumPy (as of 1.14):
1. Creating :term:`structured datatypes <structured data type>` with
:term:`fields <field>` aligned ... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/dev/development_environment.rst | .. _development-environment:
Setting up and using your development environment
=================================================
.. _recommended-development-setup:
Recommended development setup
-----------------------------
Since NumPy contains parts written in C and Cython that need to be
compiled before use, make... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/dev/underthehood.rst | .. _underthehood:
===========================================
Under-the-hood documentation for developers
===========================================
These documents are intended as a low-level look into NumPy; focused
towards developers.
.. toctree::
:maxdepth: 1
internals
internals.code-explanations
a... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/dev/gitwash_links.txt | .. _NumPy: https://www.numpy.org
.. _`NumPy github`: https://github.com/numpy/numpy
.. _`NumPy mailing list`: https://scipy.org/scipylib/mailing-lists.html
| 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/dev/index.rst | .. _devindex:
#####################
Contributing to NumPy
#####################
Not a coder? Not a problem! NumPy is multi-faceted, and we can use a lot of help.
These are all activities we'd like to get help with (they're all important, so
we list them in alphabetical order):
- Code maintenance and development
- Co... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/dev/internals.rst | .. currentmodule:: numpy
.. _numpy-internals:
*************************************
Internal organization of NumPy arrays
*************************************
It helps to understand a bit about how NumPy arrays are handled under the covers
to help understand NumPy better. This section will not go into great detail.... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/dev/depending_on_numpy.rst | .. _for-downstream-package-authors:
For downstream package authors
==============================
This document aims to explain some best practices for authoring a package that
depends on NumPy.
Understanding NumPy's versioning and API/ABI stability
------------------------------------------------------
NumPy uses... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/dev/development_workflow.rst | .. _development-workflow:
====================
Development workflow
====================
You already have your own forked copy of the NumPy_ repository, by
following :ref:`forking`, :ref:`set-up-fork`, you have configured git_
by following :ref:`configure-git`, and have linked the upstream
repository as explained in ... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/dev/internals.code-explanations.rst | .. currentmodule:: numpy
.. _c-code-explanations:
*************************
NumPy C code explanations
*************************
Fanaticism consists of redoubling your efforts when you have forgotten
your aim.
--- *George Santayana*
An authority is a person who can tell you more about something than
... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/dev/howto_build_docs.rst | .. _howto-build-docs:
=========================================
Building the NumPy API and reference docs
=========================================
If you only want to get the documentation, note that pre-built
versions can be found at
https://numpy.org/doc/
in several different formats.
Development environments
=... | 0 |
public_repos/numpy/doc/source/dev | public_repos/numpy/doc/source/dev/governance/governance.rst | ================================================================
NumPy project governance and decision-making
================================================================
The purpose of this document is to formalize the governance process
used by the NumPy project in both ordinary and extraordinary
situations, a... | 0 |
public_repos/numpy/doc/source/dev | public_repos/numpy/doc/source/dev/governance/index.rst | #####################
NumPy governance
#####################
.. toctree::
:maxdepth: 3
governance
| 0 |
public_repos/numpy/doc/source/dev | public_repos/numpy/doc/source/dev/gitwash/following_latest.rst | .. _following-latest:
These are the instructions if you just want to follow the latest
*NumPy* source, but you don't need to do any development for now.
If you do want to contribute a patch (excellent!) or do more extensive
NumPy development, see :ref:`development-workflow`.
The steps are:
* :ref:`install-git`
* get... | 0 |
public_repos/numpy/doc/source/dev | public_repos/numpy/doc/source/dev/gitwash/git_intro.rst | Install git
===========
Developing with git can be done entirely without github. Git is a distributed
version control system. In order to use git on your machine you must `install
it`_.
.. include:: git_links.inc
| 0 |
public_repos/numpy/doc/source/dev | public_repos/numpy/doc/source/dev/gitwash/dot2_dot3.rst | .. _dot2-dot3:
========================================
Two and three dots in difference specs
========================================
Thanks to Yarik Halchenko for this explanation.
Imagine a series of commits A, B, C, D... Imagine that there are two
branches, *topic* and *main*. You branched *topic* off *main*... | 0 |
public_repos/numpy/doc/source/dev | public_repos/numpy/doc/source/dev/gitwash/development_setup.rst | .. _development-setup:
##############################################################################
Setting up git for NumPy development
##############################################################################
To contribute code or documentation, you first need
#. git installed on your machine
#. a GitHub ac... | 0 |
public_repos/numpy/doc/source/dev | public_repos/numpy/doc/source/dev/gitwash/index.rst | .. _using-git:
.. _git-development:
=====================
Git for development
=====================
These pages describe a general git_ and github_ workflow.
This is not a comprehensive git_ reference. It's tailored to the github_
hosting service. You may well find better or quicker ways of getting stuff done
with ... | 0 |
public_repos/numpy/doc/source/dev | public_repos/numpy/doc/source/dev/gitwash/configure_git.rst | .. _configure-git:
=================
Git configuration
=================
.. _git-config-basic:
Overview
========
Your personal git_ configurations are saved in the ``.gitconfig`` file in
your home directory.
Here is an example ``.gitconfig`` file::
[user]
name = Your Name
email = you@yourdoma... | 0 |
public_repos/numpy/doc/source/dev | public_repos/numpy/doc/source/dev/gitwash/git_links.inc | .. This (-*- rst -*-) format file contains commonly used link targets
and name substitutions. It may be included in many files,
therefore it should only contain link targets and name
substitutions. Try grepping for "^\.\. _" to find plausible
candidates for this list.
.. NOTE: reST targets are
__not_c... | 0 |
public_repos/numpy/doc/source/dev | public_repos/numpy/doc/source/dev/gitwash/git_resources.rst | .. _git-resources:
=========================
Additional Git_ resources
=========================
Tutorials and summaries
=======================
* `github help`_ has an excellent series of how-to guides.
* `learn.github`_ has an excellent series of tutorials
* The `pro git book`_ is a good in-depth book on git.
* Th... | 0 |
public_repos/numpy/doc/source/dev | public_repos/numpy/doc/source/dev/examples/doxy_rst.h | /**
* A comment block contains reST markup.
* @rst
* .. note::
*
* Thanks to Breathe_, we were able to bring it to Doxygen_
*
* Some code example::
*
* int example(int x) {
* return x * 2;
* }
* @endrst
*/
void doxy_reST_example(void);
| 0 |
public_repos/numpy/doc/source/dev | public_repos/numpy/doc/source/dev/examples/doxy_func.h | /**
* This a simple brief.
*
* And the details goes here.
* Multi lines are welcome.
*
* @param num leave a comment for parameter num.
* @param str leave a comment for the second parameter.
* @return leave a comment for the returned value.
*/
int doxy_javadoc_example(int num, const char *str);
| 0 |
public_repos/numpy/doc/source/dev | public_repos/numpy/doc/source/dev/examples/.doxyfile | INPUT += @CUR_DIR
INCLUDE_PATH += @CUR_DIR
| 0 |
public_repos/numpy/doc/source/dev | public_repos/numpy/doc/source/dev/examples/doxy_class.hpp | /**
* Template to represent limbo numbers.
*
* Specializations for integer types that are part of nowhere.
* It doesn't support with any real types.
*
* @param Tp Type of the integer. Required to be an integer type.
* @param N Number of elements.
*/
template<typename Tp, std::size_t N>
class DoxyLimbo {
p... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/f2py/f2py-examples.rst | .. _f2py-examples:
F2PY examples
=============
Below are some examples of F2PY usage. This list is not comprehensive, but can
be used as a starting point when wrapping your own code.
F2PY walkthrough: a basic extension module
------------------------------------------
Creating source for a basic extension module
~~... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/f2py/f2py.getting-started.rst | .. _f2py-getting-started:
======================================
Three ways to wrap - getting started
======================================
Wrapping Fortran or C functions to Python using F2PY consists of the
following steps:
* Creating the so-called :doc:`signature file <signature-file>` that contains
descripti... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/f2py/f2py-testing.rst | .. _f2py-testing:
===============
F2PY test suite
===============
F2PY's test suite is present in the directory ``numpy/f2py/tests``. Its aim
is to ensure that Fortran language features are correctly translated to Python.
For example, the user can specify starting and ending indices of arrays in
Fortran. This behavio... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/f2py/usage.rst | ===========
Using F2PY
===========
This page contains a reference to all command-line options for the ``f2py``
command, as well as a reference to internal functions of the ``numpy.f2py``
module.
Using ``f2py`` as a command-line tool
=====================================
When used as a command-line tool, ``f2py`` has... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/f2py/f2py-reference.rst | .. _f2py-reference:
F2PY reference manual
=====================
.. toctree::
:maxdepth: 2
signature-file
python-usage
buildtools/index
advanced
f2py-testing
| 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/f2py/index.rst | .. _f2py:
=====================================
F2PY user guide and reference manual
=====================================
The purpose of the ``F2PY`` --*Fortran to Python interface generator*-- utility
is to provide a connection between Python and Fortran. F2PY is a part of NumPy_
(``numpy.f2py``) and also available... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/f2py/signature-file.rst | ==================
Signature file
==================
The interface definition file (.pyf) is how you can fine-tune the interface
between Python and Fortran. The syntax specification for signature files
(``.pyf`` files) is modeled on the Fortran 90/95 language specification. Almost
all Fortran 90/95 standard construct... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/f2py/python-usage.rst | ==================================
Using F2PY bindings in Python
==================================
In this page, you can find a full description and a few examples of common usage
patterns for F2PY with Python and different argument types. For more examples
and use cases, see :ref:`f2py-examples`.
Fortran type objec... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/f2py/advanced.rst | ========================
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 ... | 0 |
public_repos/numpy/doc/source | public_repos/numpy/doc/source/f2py/f2py-user.rst | .. _f2py-user:
F2PY user guide
===============
.. toctree::
:maxdepth: 2
f2py.getting-started
usage
f2py-examples | 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/windows/conda.rst | .. _f2py-win-conda:
=========================
F2PY and Conda on Windows
=========================
As a convenience measure, we will additionally assume the
existence of ``scoop``, which can be used to install tools without
administrative access.
.. code-block:: powershell
Invoke-Expression (New-Object System.Net.... | 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/windows/pgi.rst | .. _f2py-win-pgi:
===============================
F2PY and PGI Fortran on Windows
===============================
A variant of these are part of the so called "classic" Flang, however,
as classic Flang requires a custom LLVM and compilation from sources.
.. warning::
Since the proprietary compilers are no longer ... | 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/windows/index.rst | .. _f2py-windows:
=================
F2PY and Windows
=================
.. warning::
F2PY support for Windows is not always at par with Linux support
.. note::
`ScPy's documentation`_ has some information on system-level dependencies
which are well tested for Fortran as well.
Broadly speaking, there are two ... | 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/windows/intel.rst | .. _f2py-win-intel:
==============================
F2PY and Windows Intel Fortran
==============================
As of NumPy 1.23, only the classic Intel compilers (``ifort``) are supported.
.. note::
The licensing restrictions for beta software `have been relaxed`_ during
the transition to the LLVM backed ``ifx/... | 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/windows/msys2.rst | .. _f2py-win-msys2:
===========================
F2PY and Windows with MSYS2
===========================
Follow the standard `installation instructions`_. Then, to grab the requisite Fortran compiler with ``MVSC``:
.. code-block:: bash
# Assuming a fresh install
pacman -Syu # Restart the terminal
pacman -Su... | 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/ftype.f | C FILE: FTYPE.F
SUBROUTINE FOO(N)
INTEGER N
Cf2py integer optional,intent(in) :: n = 13
REAL A,X
COMMON /DATA/ A,X(3)
C PRINT*, "IN FOO: N=",N," A=",A," X=[",X(1),X(2),X(3),"]"
END
C END OF FTYPE.F
| 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/moddata.f90 | module mod
integer i
integer :: x(4)
real, dimension(2,3) :: a
real, allocatable, dimension(:,:) :: b
contains
subroutine foo
integer k
print*, "i=",i
print*, "x=[",x,"]"
print*, "a=["
print*, "[",a(1,1),",",a(1,2),",",a(1,3),"]"
print*, "[",a(2,1),",",a(2,2),",",a(2,3),"]"
print*... | 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/add-test.f | subroutine addb(k)
real(8), intent(inout) :: k(:)
k=k+1
endsubroutine
subroutine addc(w,k)
real(8), intent(in) :: w(:)
real(8), intent(out) :: k(size(w))
k=w+1
endsubroutine | 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/callback2.pyf | ! -*- f90 -*-
python module __user__routines
interface
function fun(i) result (r)
integer :: i
real*8 :: r
end function fun
end interface
end python module __user__routines
python module callback2
interface
subroutine foo(f,r)
use __user__rout... | 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/meson_upd.build | project('f2py_examples', 'c',
version : '0.1',
license: 'BSD-3',
meson_version: '>=0.64.0',
default_options : ['warning_level=2'],
)
add_languages('fortran')
py_mod = import('python')
py = py_mod.find_installation(pure: false)
py_dep = py.dependency()
incdir_numpy = run_command(py,
['-c', 'import os; os.ch... | 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/fib1.f | C FILE: FIB1.F
SUBROUTINE FIB(A,N)
C
C CALCULATE FIRST N FIBONACCI NUMBERS
C
INTEGER N
REAL*8 A(N)
DO I=1,N
IF (I.EQ.1) THEN
A(I) = 0.0D0
ELSEIF (I.EQ.2) THEN
A(I) = 1.0D0
ELSE
A(I) = A(I-1) + A(I-2)
ENDIF
ENDDO
... | 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/CMakeLists_skbuild.txt | ### setup project ###
cmake_minimum_required(VERSION 3.9)
project(fibby
VERSION 1.0
DESCRIPTION "FIB module"
LANGUAGES C Fortran
)
# Safety net
if(PROJECT_SOURCE_DIR STREQUAL PROJECT_BINARY_DIR)
message(
FATAL_ERROR
"In-source builds not allowed. Please make a new directory (called a build directo... | 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/setup_example.py | from numpy.distutils.core import Extension
ext1 = Extension(name = 'scalar',
sources = ['scalar.f'])
ext2 = Extension(name = 'fib2',
sources = ['fib2.pyf', 'fib1.f'])
if __name__ == "__main__":
from numpy.distutils.core import setup
setup(name = 'f2py_example',
desc... | 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/meson.build | project('f2py_examples', 'c',
version : '0.1',
license: 'BSD-3',
meson_version: '>=0.64.0',
default_options : ['warning_level=2'],
)
add_languages('fortran')
py_mod = import('python')
py = py_mod.find_installation(pure: false)
py_dep = py.dependency()
incdir_numpy = run_command(py,
['-c', 'import os; os.ch... | 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/fib3.f | C FILE: FIB3.F
SUBROUTINE FIB(A,N)
C
C CALCULATE FIRST N FIBONACCI NUMBERS
C
INTEGER N
REAL*8 A(N)
Cf2py intent(in) n
Cf2py intent(out) a
Cf2py depend(n) a
DO I=1,N
IF (I.EQ.1) THEN
A(I) = 0.0D0
ELSEIF (I.EQ.2) THEN
A(I) = 1.0D0
ELSE
... | 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/fib1.pyf | ! -*- f90 -*-
python module fib2 ! in
interface ! in :fib2
subroutine fib(a,n) ! in :fib2:fib1.f
real*8 dimension(n) :: a
integer optional,check(len(a)>=n),depend(a) :: n=len(a)
end subroutine fib
end interface
end python module fib2
! This file was auto-generated ... | 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/setup_skbuild.py | from skbuild import setup
setup(
name="fibby",
version="0.0.1",
description="a minimal example package (fortran version)",
license="MIT",
packages=['fibby'],
python_requires=">=3.7",
)
| 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/allocarr.f90 | module mod
real, allocatable, dimension(:,:) :: b
contains
subroutine foo
integer k
if (allocated(b)) then
print*, "b=["
do k = 1,size(b,1)
print*, b(k,1:size(b,2))
enddo
print*, "]"
else
print*, "b is not allocated"
endif
end subroutine foo
end module... | 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/myroutine.pyf | ! -*- f90 -*-
! Note: the context of this file is case sensitive.
python module myroutine ! in
interface ! in :myroutine
subroutine s(n,m,c,x) ! in :myroutine:myroutine.f90
integer intent(in) :: n
integer intent(in) :: m
real(kind=8) dimension(:),intent(in) :: c
... | 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/add-improved.f | C
SUBROUTINE ZADD(A,B,C,N)
C
CF2PY INTENT(OUT) :: C
CF2PY INTENT(HIDE) :: N
CF2PY DOUBLE COMPLEX :: A(N)
CF2PY DOUBLE COMPLEX :: B(N)
CF2PY DOUBLE COMPLEX :: C(N)
DOUBLE COMPLEX A(*)
DOUBLE COMPLEX B(*)
DOUBLE COMPLEX C(*)
INTEGER N
DO 20 J = 1, N
C(J) = A(J) + B(J)
20 CO... | 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/add.pyf | subroutine zadd(a,b,c,n) ! in :add:add.f
double complex dimension(*) :: a
double complex dimension(*) :: b
double complex dimension(*) :: c
integer :: n
end subroutine zadd | 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/scalar.f | C FILE: SCALAR.F
SUBROUTINE FOO(A,B)
REAL*8 A, B
Cf2py intent(in) a
Cf2py intent(inout) b
PRINT*, " A=",A," B=",B
PRINT*, "INCREMENT A AND B"
A = A + 1D0
B = B + 1D0
PRINT*, "NEW A=",A," B=",B
END
C END OF FILE SCALAR.F
| 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/var.pyf | ! -*- f90 -*-
python module var
usercode '''
int BAR = 5;
'''
interface
usercode '''
PyDict_SetItemString(d,"BAR",PyInt_FromLong(BAR));
'''
end interface
end python module
| 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/extcallback.f | subroutine f1()
print *, "in f1, calling f2 twice.."
call f2()
call f2()
return
end
subroutine f2()
cf2py intent(callback, hide) fpy
external fpy
print *, "in f2, calling f2py.."
call fpy()
return
end
| 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/fib2.pyf | ! -*- f90 -*-
python module fib2
interface
subroutine fib(a,n)
real*8 dimension(n),intent(out),depend(n) :: a
integer intent(in) :: n
end subroutine fib
end interface
end python module fib2
| 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/spam.pyf | ! -*- f90 -*-
python module spam
usercode '''
static char doc_spam_system[] = "Execute a shell command.";
static PyObject *spam_system(PyObject *self, PyObject *args)
{
char *command;
int sts;
if (!PyArg_ParseTuple(args, "s", &command))
return NULL;
sts = system(command);
retur... | 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/common.f | C FILE: COMMON.F
SUBROUTINE FOO
INTEGER I,X
REAL A
COMMON /DATA/ I,X(4),A(2,3)
PRINT*, "I=",I
PRINT*, "X=[",X,"]"
PRINT*, "A=["
PRINT*, "[",A(1,1),",",A(1,2),",",A(1,3),"]"
PRINT*, "[",A(2,1),",",A(2,2),",",A(2,3),"]"
PRINT*, "]"
END
C END OF COMMON.F
| 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/asterisk1.f90 | subroutine foo1(s)
character*(*), intent(out) :: s
!f2py character(f2py_len=12) s
s = "123456789A12"
end subroutine foo1
| 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/asterisk2.f90 | subroutine foo2(s, n)
character(len=*), intent(out) :: s
integer, intent(in) :: n
!f2py character(f2py_len=n), depend(n) :: s
s = "123456789A123456789B"(1:n)
end subroutine foo2
| 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/string.f | C FILE: STRING.F
SUBROUTINE FOO(A,B,C,D)
CHARACTER*5 A, B
CHARACTER*(*) C,D
Cf2py intent(in) a,c
Cf2py intent(inout) b,d
PRINT*, "A=",A
PRINT*, "B=",B
PRINT*, "C=",C
PRINT*, "D=",D
PRINT*, "CHANGE A,B,C,D"
A(1:1) = 'A'
B(1:1) = 'B'
C(1:1) = 'C'
D(1... | 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/CMakeLists.txt | cmake_minimum_required(VERSION 3.18) # Needed to avoid requiring embedded Python libs too
project(fibby
VERSION 1.0
DESCRIPTION "FIB module"
LANGUAGES C Fortran
)
# Safety net
if(PROJECT_SOURCE_DIR STREQUAL PROJECT_BINARY_DIR)
message(
FATAL_ERROR
"In-source builds not allowed. Please make a new dir... | 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/array.f | C FILE: ARRAY.F
SUBROUTINE FOO(A,N,M)
C
C INCREMENT THE FIRST ROW AND DECREMENT THE FIRST COLUMN OF A
C
INTEGER N,M,I,J
REAL*8 A(N,M)
Cf2py intent(in,out,copy) a
Cf2py integer intent(hide),depend(a) :: n=shape(a,0), m=shape(a,1)
DO J=1,M
A(1,J) = A(1,J) + 1D0
ENDDO
DO I=... | 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/myroutine-edited.pyf | ! -*- f90 -*-
! Note: the context of this file is case sensitive.
python module myroutine ! in
interface ! in :myroutine
subroutine s(n,m,c,x) ! in :myroutine:myroutine.f90
integer intent(in) :: n
integer intent(in) :: m
real(kind=8) dimension(:),intent(in) :: c
... | 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/f2cmap_demo.f | subroutine func1(n, x, res)
use, intrinsic :: iso_fortran_env, only: int64, real64
implicit none
integer(int64), intent(in) :: n
real(real64), intent(in) :: x(n)
real(real64), intent(out) :: res
Cf2py intent(hide) :: n
res = sum(x)
end
| 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/add.f | C
SUBROUTINE ZADD(A,B,C,N)
C
DOUBLE COMPLEX A(*)
DOUBLE COMPLEX B(*)
DOUBLE COMPLEX C(*)
INTEGER N
DO 20 J = 1, N
C(J) = A(J)+B(J)
20 CONTINUE
END
| 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/add-edited.pyf | subroutine zadd(a,b,c,n) ! in :add:add.f
double complex dimension(n) :: a
double complex dimension(n) :: b
double complex intent(out),dimension(n) :: c
integer intent(hide),depend(a) :: n=len(a)
end subroutine zadd | 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/callback.f | C FILE: CALLBACK.F
SUBROUTINE FOO(FUN,R)
EXTERNAL FUN
INTEGER I
REAL*8 R, FUN
Cf2py intent(out) r
R = 0D0
DO I=-5,5
R = R + FUN(I)
ENDDO
END
C END OF FILE CALLBACK.F
| 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/filter.f | C
SUBROUTINE DFILTER2D(A,B,M,N)
C
DOUBLE PRECISION A(M,N)
DOUBLE PRECISION B(M,N)
INTEGER N, M
CF2PY INTENT(OUT) :: B
CF2PY INTENT(HIDE) :: N
CF2PY INTENT(HIDE) :: M
DO 20 I = 2,M-1
DO 40 J = 2,N-1
B(I,J) = A(I,J) +
& (A(I-1,J)+A(I+1,J) +
& ... | 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/calculate.f | subroutine calculate(x,n)
cf2py intent(callback) func
external func
c The following lines define the signature of func for F2PY:
cf2py real*8 y
cf2py y = func(y)
c
cf2py intent(in,out,copy) x
integer n,i
real*8 x(n), func
do i=1,n
x(i) = func(x(i))
end do
end
| 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/pyproj_skbuild.toml | [build-system]
requires = ["setuptools>=42", "wheel", "scikit-build", "cmake>=3.9", "numpy>=1.21"]
build-backend = "setuptools.build_meta"
| 0 |
public_repos/numpy/doc/source/f2py | public_repos/numpy/doc/source/f2py/code/myroutine.f90 | subroutine s(n, m, c, x)
implicit none
integer, intent(in) :: n, m
real(kind=8), intent(out), dimension(n,m) :: x
real(kind=8), intent(in) :: c(:)
x = 0.0d0
x(1, 1) = c(1)
end subroutine s | 0 |
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