From 1fd1eb612e00884ae4c42083a77b470920b566a2 Mon Sep 17 00:00:00 2001
From: Jean-Baptiste Keck <Jean-Baptiste.Keck@imag.fr>
Date: Tue, 3 Nov 2020 20:22:35 +0100
Subject: [PATCH] clean up build files
---
CMakeLists.txt | 182 +-
CTestConfig.cmake | 6 -
HySoPConfig.cmake.in | 38 -
HySoPConfigVersion.cmake.in | 12 -
cmake/MyTools.cmake | 8 -
config.hpp.cmake => cmake/config.hpp.cmake | 0
f2py_f2cmap.in => cmake/f2py_f2cmap.in | 0
opencl_explore.py => cmake/opencl_explore.py | 0
precision.conf.in => cmake/precision.conf.in | 0
sort_f90.py => cmake/sort_f90.py | 0
notebooks/.gitignore | 2 -
notebooks/00_introduction.ipynb | 1381 --------
notebooks/01_analytical.ipynb | 249 --
notebooks/videos/analytic.ogv | Bin 303949 -> 0 bytes
setup.py.in | 97 -
src/CMakeLists.txt | 35 +-
swig/cpp2hysop.i | 7 -
swig/hysop++/domain.i | 38 -
swig/hysop++/fft.i | 94 -
swig/hysop++/hysop++.i | 5 -
swig/hysop++/utils.i | 29 -
swig/numpy.i | 3085 ------------------
swig/start.i | 14 -
23 files changed, 29 insertions(+), 5253 deletions(-)
delete mode 100644 CTestConfig.cmake
delete mode 100644 HySoPConfig.cmake.in
delete mode 100644 HySoPConfigVersion.cmake.in
rename config.hpp.cmake => cmake/config.hpp.cmake (100%)
rename f2py_f2cmap.in => cmake/f2py_f2cmap.in (100%)
rename opencl_explore.py => cmake/opencl_explore.py (100%)
rename precision.conf.in => cmake/precision.conf.in (100%)
rename sort_f90.py => cmake/sort_f90.py (100%)
delete mode 100644 notebooks/.gitignore
delete mode 100644 notebooks/00_introduction.ipynb
delete mode 100644 notebooks/01_analytical.ipynb
delete mode 100644 notebooks/videos/analytic.ogv
delete mode 100644 swig/cpp2hysop.i
delete mode 100644 swig/hysop++/domain.i
delete mode 100644 swig/hysop++/fft.i
delete mode 100644 swig/hysop++/hysop++.i
delete mode 100644 swig/hysop++/utils.i
delete mode 100644 swig/numpy.i
delete mode 100644 swig/start.i
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 3a7f7df9e..312a6db25 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -39,25 +39,22 @@ option(DOUBLEPREC "Set default HySoP floating point precision. Default = on, ie
option(USE_MPI "Compile and link HySoP with mpi when this mode is enable. Default = on. WARNING: off mode is bugged." ON)
option(WITH_TESTS "Enable testing. Default = OFF" ON)
option(BUILD_SHARED_LIBS "Enable dynamic library build, default = ON." ON)
-option(USE_CXX "Expand hysop with some new functions from a generated c++ to python interface, wrapped into hysop.cpp2hysop module. Default = ON." OFF)
option(WITH_SCALES "compile/create scales lib and link it with HySoP. Default = ON." ON)
option(WITH_PARALLEL_COMPRESSED_HDF5 "Try to enable parallel compressed hdf5 interface. Default = ON." ON)
option(WITH_FFTW "Link with fftw library (required for some HySoP solvers), default = ON" ON)
option(WITH_EXTRAS "Link with some extra fortran libraries (like arnoldi solver), default = OFF" OFF)
option(WITH_OPENCL "Use of GPU (required for some HySoP solvers), default = ON" ON)
-option(WITH_MAIN_FORTRAN "Create an executable (test purpose) from fortran sources in src/main, linked with libhysop, default = OFF" OFF)
-option(WITH_MAIN_CXX "Create an executable (test purpose) from cxx sources in src/hysop++/main, linked with libhysop, default = OFF" OFF)
option(PROFILE "Enable profiling mode for HySoP. Can also be enabled with HYSOP_PROFILE environment variable. Default = OFF" OFF)
option(VERBOSE "Enable verbose mode for HySoP. Can also be enabled with HYSOP_VERBOSE environment variable. Default = OFF" OFF)
option(DEBUG "Enable debug mode for HySoP. Can also be enabled with HYSOP_DEBUG environment variable. Default = OFF" OFF)
option(FULL_TEST "Enable all test options (pep8, mpi ...) - Default = OFF" OFF)
option(OPTIM "To allow python -OO run, some packages must be deactivated. Set this option to 'ON' to do so. Default = OFF" OFF)
option(WITH_MPI_TESTS "Enable mpi tests. Default = OFF." OFF)
-option(WITH_GOOGLE_TESTS "Enable google tests (c++). Default = OFF." OFF)
option(FORTRAN_LAYOUT "Choose default data layout ('fortran', column-major or 'C' order, row-major) for arrays. Default = column-major." OFF)
option(WITH_DOCUMENTATION "Build Documentation. Default = OFF" OFF)
option(ENABLE_LONG_TESTS "Enable tests that may run for long time with important memory print. Default = OFF." OFF)
option(DEV_MODE "Enable devel mode (aggressive checking of warnings ..). Default = ON." ON)
+
# Set python install mode:
# - user --> behave as 'python setup.py install --user'
# - standard --> install in python site-package (ie behave as python setup.py install)
@@ -68,6 +65,7 @@ set(HYSOP_INSTALL "user" CACHE STRING "Install mode for hysop python package")
# http://public.kitware.com/Bug/view.php?id=11964
# See also http://www.cmake.org/cmake/help/v3.0/module/GNUInstallDirs.html?highlight=gnuinstalldirs
include(GNUInstallDirs)
+
# Set prefix path for libraries installation
# --> means that any library target will be installed
# in CMAKE_INSTALL_PREFIX/_install_lib
@@ -89,11 +87,6 @@ if(WITH_FFTW OR WITH_SCALES OR WITH_EXTRAS)
set(USE_FORTRAN "ON")
endif()
-if(NOT USE_CXX)
- set(WITH_MAIN_CXX "OFF")
- set(WITH_GOOGLE_TESTS "OFF")
-endif()
-
# Force a default build type if not provided by user
# CMAKE_BUILD_TYPE = empty, Debug, Release, RelWithDebInfo or MinSizeRel.
if(NOT CMAKE_BUILD_TYPE)
@@ -104,11 +97,13 @@ endif()
# cmake project name
set(PROJECT_NAME hysop)
-# --- Name for the package ---
+
# This name will be used as the Python Package name
set(PACKAGE_NAME "hysop")
+
# --- The name (without extension) of the lib to be created ---
set(PROJECT_LIBRARY_NAME ${PROJECT_NAME})
+
# ============= The project =============
# Set project name and project languages
# => this automatically defines:
@@ -117,10 +112,6 @@ set(PROJECT_LIBRARY_NAME ${PROJECT_NAME})
# Note that because of OutOfSourceBuild, binary_dir and source_dir must be different.
set(LANGLIST)
-if(USE_CXX)
- set(LANGLIST ${LANGLIST} C CXX)
-endif()
-
if(USE_FORTRAN)
set(LANGLIST ${LANGLIST} C Fortran)
endif()
@@ -137,6 +128,7 @@ set(HYSOP_LINK_LIBRARIES CACHE INTERNAL "List of external libraries.")
# - Global setup (interp and lib) -
find_package(PythonFull REQUIRED)
include(FindPythonModule)
+
# - python packages -
find_python_module(numpy REQUIRED)
find_python_module(scipy REQUIRED)
@@ -156,33 +148,27 @@ find_python_module(pyfftw REQUIRED)
find_python_module(matplotlib OPTIONAL)
find_python_module(pyvis OPTIONAL)
+# Some opencl related python package fails to import on non OpenCL machines (cluster's frontend for instance)
find_package( OpenCL )
-if(${OpenCL_LIBRARY}) # Some opencl related python package fails to import on non OpenCL machines (cluster's frontend for instance)
- find_python_module(pyopencl REQUIRED)
- find_python_module(gpyfft REQUIRED)
+if(${OpenCL_LIBRARY})
+ find_python_module(pyopencl REQUIRED)
+ find_python_module(gpyfft REQUIRED)
else()
- find_python_module(pyopencl )
- find_python_module(gpyfft )
+ find_python_module(pyopencl)
+ find_python_module(gpyfft)
endif()
+
# --- MPI ---
if(USE_MPI)
find_package(MPI REQUIRED)
find_python_module(mpi4py REQUIRED)
endif()
+
# --- Wheel, required for a proper build/install process ---
find_python_module(wheel REQUIRED)
-if(USE_CXX)
- find_package(SWIG 3.0.2 REQUIRED)
- # WARNING FP : for cmake < 3.0 UseSWIG.cmake
- # does not work properly (bug for swig outdir)
- if(CMAKE_VERSION VERSION_LESS 3.0.0)
- set(SWIG_USE_FILE ${CMAKE_SOURCE_DIR}/cmake/UseSWIG.cmake)
- endif()
- include(${SWIG_USE_FILE})
-endif()
# Find python build dir name --> needed for tests and doc
-if(USE_CXX OR USE_FORTRAN)
+if(USE_FORTRAN)
execute_process(
COMMAND ${PYTHON_EXECUTABLE} -c "import distutils.util as ut ; import distutils.sysconfig as sy; print('lib.'+ut.get_platform()+'-'+sy.get_python_version())"
OUTPUT_VARIABLE ${PROJECT_NAME}_PYTHON_BUILD_DIR)
@@ -218,10 +204,6 @@ if(WITH_FFTW)
add_definitions(${FFTW_DEFINES})
endif()
-if(USE_CXX)
- compile_with(Boost REQUIRED)
-endif()
-
if(WITH_EXTRAS)
# Arnoldi solver needs zgeev, which means lapack
compile_with(LAPACK)
@@ -233,7 +215,6 @@ if(WITH_EXTRAS)
endif()
endforeach()
set(EXTRASLIB ${dirlist} CACHE PATH "extras libraries dir")
-
endif()
# ========= Check parallel hdf5 availability =========
@@ -259,15 +240,15 @@ endif()
# ========= Check which opencl devices are available on the system =========
if(WITH_OPENCL)
execute_process(
- COMMAND ${PYTHON_EXECUTABLE} ${CMAKE_SOURCE_DIR}/opencl_explore.py "EXPLORE")
+ COMMAND ${PYTHON_EXECUTABLE} ${CMAKE_SOURCE_DIR}/cmake/opencl_explore.py "EXPLORE")
execute_process(
- COMMAND ${PYTHON_EXECUTABLE} ${CMAKE_SOURCE_DIR}/opencl_explore.py
+ COMMAND ${PYTHON_EXECUTABLE} ${CMAKE_SOURCE_DIR}/cmake/opencl_explore.py
OUTPUT_VARIABLE OPENCL_DEFAULT_OPENCL_ID)
else()
execute_process(
- COMMAND ${PYTHON_EXECUTABLE} ${CMAKE_SOURCE_DIR}/opencl_explore.py "EXPLORE" CPU)
+ COMMAND ${PYTHON_EXECUTABLE} ${CMAKE_SOURCE_DIR}/cmake/opencl_explore.py "EXPLORE" CPU)
execute_process(
- COMMAND ${PYTHON_EXECUTABLE} ${CMAKE_SOURCE_DIR}/opencl_explore.py CPU
+ COMMAND ${PYTHON_EXECUTABLE} ${CMAKE_SOURCE_DIR}/cmake/opencl_explore.py CPU
OUTPUT_VARIABLE OPENCL_DEFAULT_OPENCL_ID)
endif()
@@ -276,6 +257,8 @@ list(GET MY_LIST 0 OPENCL_DEFAULT_OPENCL_PLATFORM_ID)
list(GET MY_LIST 1 OPENCL_DEFAULT_OPENCL_DEVICE_ID)
display(OPENCL_DEFAULT_OPENCL_PLATFORM_ID)
display(OPENCL_DEFAULT_OPENCL_DEVICE_ID)
+
+
# =========== RPATH stuff ===========
# Doc :
# - https://cmake.org/Wiki/CMake_RPATH_handling
@@ -323,7 +306,6 @@ else()
endif()
# ====== Create (and setup) install/uninstall targets ======
-#
# --> set installation dir
# --> set options for python install
# --> create install/uninstall targets
@@ -365,19 +347,17 @@ endif()
if(USE_FORTRAN)
-
# Path to fortran-related generated files (.pyf, precision ...)
set(GENERATED_FORTRAN_FILES_DIR ${CMAKE_BINARY_DIR}/generated_fortran/)
-
# --- copy sort_f90 file to build ---
# Required for setup.py to handle fortran files dependencies
- configure_file(${CMAKE_SOURCE_DIR}/sort_f90.py ${CMAKE_BINARY_DIR}/sort_f90.py)
+ configure_file(${CMAKE_SOURCE_DIR}/cmake/sort_f90.py ${CMAKE_BINARY_DIR}/sort_f90.py)
# --- Generate f2py_f2cmap file ---
- if(EXISTS ${CMAKE_SOURCE_DIR}/f2py_f2cmap.in)
+ if(EXISTS ${CMAKE_SOURCE_DIR}/cmake/f2py_f2cmap.in)
message(STATUS "Generate f2py map file ...")
- configure_file(${CMAKE_SOURCE_DIR}/f2py_f2cmap.in
+ configure_file(${CMAKE_SOURCE_DIR}/cmake/f2py_f2cmap.in
${CMAKE_BINARY_DIR}/.f2py_f2cmap)
endif()
@@ -420,11 +400,11 @@ if(USE_FORTRAN)
set(Fortran_FLAGS ${CMAKE_Fortran_FLAGS})
append_flags(Fortran_FLAGS ${CMAKE_Fortran_FLAGS_${CMAKE_BUILD_TYPE}})
# --- Generate precision.f95 and precision.pyf files ---
- if(EXISTS ${CMAKE_SOURCE_DIR}/precision.conf.in)
+ if(EXISTS ${CMAKE_SOURCE_DIR}/cmake/precision.conf.in)
message(STATUS "Generate precision.f95 file ...")
- configure_file(${CMAKE_SOURCE_DIR}/precision.conf.in
+ configure_file(${CMAKE_SOURCE_DIR}/cmake/precision.conf.in
${GENERATED_FORTRAN_FILES_DIR}/precision.f95)
- configure_file(${CMAKE_SOURCE_DIR}/precision.conf.in
+ configure_file(${CMAKE_SOURCE_DIR}/cmake/precision.conf.in
${GENERATED_FORTRAN_FILES_DIR}/precision.pyf)
endif()
endif()
@@ -434,42 +414,6 @@ if(USE_FORTRAN)
set(FORTRAN_INCLUDE_DIRS ${FFTW_INCLUDE_DIRS})
endif()
-if(USE_CXX)
- #C++ variables used by setup.py.in for swig
- if(DEV_MODE)
- if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")
- set(CXX_WARNING_FLAGS "-W -Wall -Wextra -Wno-unused-variable -Wno-unused-parameter -Wno-unused-local-typedefs -Wno-missing-braces")
- elseif ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")
- set(CXX_WARNING_FLAGS "-W -Wall -Wextra -Wno-unused-variable -Wno-unused-but-set-variable -Wno-unused-parameter -Wno-unused-local-typedefs -Wno-deprecated-declarations")
- #elseif ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Intel")
- #elseif ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "MSVC")
- else()
- set(CXX_WARNING_FLAGS "")
- endif()
- endif()
-
- list(APPEND CMAKE_CXX_FLAGS "${CXX_WARNING_FLAGS} ${FFTW_COMPILE_FLAGS} -fPIC -std=c++11")
- if(APPLE)
- list(APPEND CMAKE_EXE_LINKER_FLAGS "-ldl -lutil")
- else()
- list(APPEND CMAKE_EXE_LINKER_FLAGS "-Wl,--no-as-needed -ldl -lutil")
- endif()
-
- set(CXX_FLAGS ${CMAKE_CXX_FLAGS})
- set(CXX_LINKER_FLAGS ${CMAKE_EXE_LINKER_FLAGS})
- set(CXX_EXTRA_DEFINES ${FFTW_DEFINES} "-DHAS_EXTERN_TEMPLATES")
-
- set(CXX_EXT_INCLUDES ${Boost_INCLUDE_DIR} ${PYTHON_INCLUDE_DIR} ${FFTW_INCLUDE_DIRS})
- set(CXX_EXT_LIBS ${Boost_LIBRARIES} ${PYTHON_LIBRARIES} ${FFTW_LIBRARIES})
- set(CXX_EXT_LIB_DIR ${Boost_LIBRARY_DIRS} ${PYTHON_LIBRARY_DIRS} ${FFTW_LIBRARY_DIRS})
-
- set(CMAKE_INCLUDE_SYSTEM_FLAG_C "-isystem ")
- set(CMAKE_INCLUDE_SYSTEM_FLAG_CXX "-isystem ")
-
- #swig package name (lib name generated by swig)
- set(CPP_2_HYSOP "cpp2hysop")
-endif()
-
# Append pythonlib to hysop link floags
list(APPEND HYSOP_LINK_LIBRARIES ${PYTHON_LIBRARIES} )
@@ -487,65 +431,6 @@ if(EXISTS ${CMAKE_SOURCE_DIR}/hysop/__init__.py.in)
configure_file(hysop/__init__.py.in ${CMAKE_SOURCE_DIR}/hysop/__init__.py)
endif()
-if(EXISTS ${CMAKE_SOURCE_DIR}/hysop/mpi/__init__.py.in)
- message(STATUS "Generate mpi/__init__.py file ...")
- file(REMOVE ${CMAKE_SOURCE_DIR}/hysop/mpi/__init__.py)
-configure_file(hysop/mpi/__init__.py.in ${CMAKE_SOURCE_DIR}/hysop/mpi/__init__.py)
-endif()
-
-# Hysop C++ library is generated in setup.py by swig
-# --- C++ main and tests ---
-if(USE_CXX)
-
- get_filename_component(CXX_DIR "${CMAKE_SOURCE_DIR}/src/hysop++" ABSOLUTE)
- get_filename_component(CXX_MAIN_DIR "${CXX_DIR}/main" ABSOLUTE)
- get_filename_component(CXX_TEST_DIR "${CXX_DIR}/tests" ABSOLUTE)
- get_filename_component(CXX_SOURCE_DIR "${CXX_DIR}/src" ABSOLUTE)
-
- include_directories(${CXX_SOURCE_DIR})
- include_directories(SYSTEM ${CXX_EXT_INCLUDES})
- link_directories(${CXX_EXT_LIB_DIRS})
- add_definitions(${CXX_EXTRA_DEFINES})
-
- if(WITH_MAIN_CXX OR WITH_GOOGLE_TESTS)
- if(APPLE) #swig only generates a bundle, need to generate another static library...
- set(HYSOP_CXX_LIBRARY_DYLIB "cpp2hysop_dylib")
-
- file(GLOB_RECURSE source_files ${CXX_SOURCE_DIR}/*.cpp)
- add_library(${HYSOP_CXX_LIBRARY_DYLIB} STATIC ${source_files})
- target_link_libraries(${HYSOP_CXX_LIBRARY_DYLIB} ${EXT_LIBRARIES})
-
- set(HYSOP_CXX_LIBRARY ${HYSOP_CXX_LIBRARY_DYLIB})
- set(HYSOP_CXX_LIBRARY_DEP cpp2hysop_dylib)
- else() #nothing to do on other platforms bundle <=> dynamic libraries, so just copy the swig generated one
- set(HYSOP_CXX_LIBRARY_BUNDLE "${CMAKE_CURRENT_BINARY_DIR}/libcpp2hysop_bundle.so")
- add_custom_target(cpp2hysop_bundle
- DEPENDS wheel
- COMMAND cp `find ${CMAKE_CURRENT_BINARY_DIR}/build -name _${CPP_2_HYSOP}.so` ${HYSOP_CXX_LIBRARY_BUNDLE}
- WORKING_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}"
- COMMENT "Copy swig c++ library to link")
- set(HYSOP_CXX_LIBRARY ${HYSOP_CXX_LIBRARY_BUNDLE})
- set(HYSOP_CXX_LIBRARY_DEP cpp2hysop_bundle)
- endif()
- endif()
-
- if(WITH_MAIN_CXX)
- list(APPEND cxx_executable_sources "${CXX_MAIN_DIR}/planner.cpp")
- list(APPEND cxx_executable_sources "${CXX_MAIN_DIR}/diffSolver.cpp")
- list(APPEND cxx_executable_sources "${CXX_MAIN_DIR}/poissonSolver.cpp")
- foreach(cxx_main_source ${cxx_executable_sources})
- get_filename_component(cxx_exec_name "${cxx_main_source}" NAME_WE)
- add_executable(${cxx_exec_name} ${cxx_main_source})
- add_dependencies(${cxx_exec_name} ${HYSOP_CXX_LIBRARY_DEP})
- target_link_libraries(${cxx_exec_name} ${HYSOP_CXX_LIBRARY} ${CXX_EXT_LIBS})
- endforeach()
- endif()
-
- if(WITH_GOOGLE_TESTS)
- add_subdirectory(${CXX_TEST_DIR})
- endif()
-endif()
-
# =========== RPATH stuff ===========
# Doc :
# - https://cmake.org/Wiki/CMake_RPATH_handling
@@ -631,11 +516,6 @@ if(VERBOSE_MODE)
else()
message(WARNING "You deactivate fortran to python interface generation. This will disable the fortran interface, including fftw and scales fonctionnalities.")
endif()
- if(USE_CXX)
- message(STATUS " CXX compiler : ${CMAKE_CXX_COMPILER}")
- else()
- message(WARNING "You deactivate c++ to python interface generation. This will disable the Aitken-Schwarz Poisson solver.")
- endif()
message(STATUS " Sources are in : ${CMAKE_SOURCE_DIR}")
message(STATUS " Build is done in : ${CMAKE_BINARY_DIR}")
message(STATUS " Project uses MPI : ${USE_MPI}")
@@ -664,11 +544,3 @@ if(VERBOSE_MODE)
message("Try to run python -c 'import hysop'. If it fails, add ${HYSOP_PYTHON_INSTALL_DIR} to PYTHONPATH environment variable.")
message("Example : \n export PYTHONPATH=${HYSOP_PYTHON_INSTALL_DIR}/:\${PYTHONPATH}\n")
endif()
-
-# Add custom target to install compiled libraries locally
-add_custom_target(update_libs
- COMMAND find ${CMAKE_SOURCE_DIR}/hysop/ -name '*.so' -type f -delete
- COMMAND find ${CMAKE_BINARY_DIR} -name '*.so' -type f -print0 | xargs -0 cp -t ${CMAKE_SOURCE_DIR}/hysop/
- COMMAND [ -f "$ENV{HOME}/.hysop.__init__.py" ] && rm ${CMAKE_SOURCE_DIR}/hysop/__init__.py
- COMMAND [ -f "$ENV{HOME}/.hysop.__init__.py" ] && cp $ENV{HOME}/.hysop.__init__.py ${CMAKE_SOURCE_DIR}/hysop/__init__.py
- DEPENDS wheel)
diff --git a/CTestConfig.cmake b/CTestConfig.cmake
deleted file mode 100644
index 93d5f9d90..000000000
--- a/CTestConfig.cmake
+++ /dev/null
@@ -1,6 +0,0 @@
-set(CTEST_PROJECT_NAME "HySoP")
-set(CTEST_NIGHTLY_START_TIME "01:00:00 UTC")
-set(CTEST_DROP_METHOD "http")
-set(CTEST_DROP_SITE "my.cdash.org")
-set(CTEST_DROP_LOCATION "/submit.php?project=HySoP")
-set(CTEST_DROP_SITE_CDASH TRUE)
diff --git a/HySoPConfig.cmake.in b/HySoPConfig.cmake.in
deleted file mode 100644
index c435c411f..000000000
--- a/HySoPConfig.cmake.in
+++ /dev/null
@@ -1,38 +0,0 @@
-# - config file for @PACKAGE_NAME@ package
-# Written by F. Pérignon, 2011 march
-#
-# This file generates @PACKAGE_NAME@Config.cmake, that may be used by another cmake project
-# to retrieve all the configuration variables from @PACKAGE_NAME@
-#
-# It defines the following variables
-#
-# @PACKAGE_NAME@_INCLUDE_DIRS - include directories for ppmcore
-# @PACKAGE_NAME@_EXTRA_INCLUDE_DIRS - path to extra headers needed for @PACKAGE_NAME@ (metis.h ...)
-# @PACKAGE_NAME@_LIBRARY_DIRS - path to @PACKAGE_NAME@ library(ies)
-# @PACKAGE_NAME@_LIBRARIES - libraries to link against to use ppmcore
-# @PACKAGE_NAME@_USE_XXX - value of option "USE_XXX" (for example USE_MPI, USE_Metis ... = ON or OFF)
-
-# Tell the user where to find ppmcore headers
-# Tell the user project where to find our headers and libraries
-set(@PACKAGE_NAME@_INCLUDE_DIRS "${${PACKAGE_SHORT_NAME}_INCLUDE_DIRS}")
-set(@PACKAGE_NAME@_EXTRA_INCLUDE_DIRS "${${PACKAGE_NAME}_EXTRA_INCLUDE_DIRS}")
-set(@PACKAGE_NAME@_LIBRARY_DIRS "${${PACKAGE_NAME}_LIB_DIR}")
-set(@PACKAGE_NAME@_MODULE_DIR "${${PACKAGE_NAME}_INCLUDE_DIRS}/Modules")
-
-# Our library dependencies (contains definitions for IMPORTED targets)
-include("${${PACKAGE_NAME}_CMAKE_DIR}/@PACKAGE_NAME@LibraryDepends.cmake")
-
-# These are IMPORTED targets created by FooBarLibraryDepends.cmake
-set(@PACKAGE_NAME@_LIBRARIES @PROJECT_LIBRARY_NAME@)
-
-# Set all @PACKAGE_NAME@ options
-set(@PACKAGE_NAME@_USE_MPI @USE_MPI@)
-set(@PACKAGE_NAME@_USE_PPM @WITH_PPM@)
-set(@PACKAGE_NAME@_USE_PPM_Numerics @WITH_PPM_Numerics@)
-
-
-# Set var for compilers used by @PACKAGE_NAME@
-#set(@PACKAGE_NAME@_Fortran_COMPILER @CMAKE_Fortran_COMPILER@)
-
-# Fortran flags
-#set(@PACKAGE_NAME@_Fortran_FLAGS @CMAKE_Fortran_FLAGS@)
diff --git a/HySoPConfigVersion.cmake.in b/HySoPConfigVersion.cmake.in
deleted file mode 100644
index ad9445c38..000000000
--- a/HySoPConfigVersion.cmake.in
+++ /dev/null
@@ -1,12 +0,0 @@
-set(PACKAGE_VERSION "@HySoP_version@")
-
-
-# Check whether the requested PACKAGE_FIND_VERSION is compatible
-if("${PACKAGE_VERSION}" VERSION_LESS "${PACKAGE_FIND_VERSION}")
- set(PACKAGE_VERSION_COMPATIBLE FALSE)
-else()
- set(PACKAGE_VERSION_COMPATIBLE TRUE)
- if ("${PACKAGE_VERSION}" VERSION_EQUAL "${PACKAGE_FIND_VERSION}")
- set(PACKAGE_VERSION_EXACT TRUE)
- endif()
-endif()
diff --git a/cmake/MyTools.cmake b/cmake/MyTools.cmake
index ac90f59e1..fb9d20ed1 100644
--- a/cmake/MyTools.cmake
+++ b/cmake/MyTools.cmake
@@ -22,14 +22,6 @@ macro(append_flags)
endmacro(append_flags)
# The use of ADD_DEFINITION results in a warning with Fortran compiler
-macro(APPEND_C_FLAGS)
- append_flags(CMAKE_C_FLAGS ${ARGV})
-endmacro(APPEND_C_FLAGS)
-
-macro(APPEND_CXX_FLAGS)
- append_flags(CMAKE_CXX_FLAGS ${ARGV})
-endmacro(APPEND_CXX_FLAGS)
-
macro(APPEND_Fortran_FLAGS)
append_flags(CMAKE_Fortran_FLAGS ${ARGV})
endmacro(APPEND_Fortran_FLAGS)
diff --git a/config.hpp.cmake b/cmake/config.hpp.cmake
similarity index 100%
rename from config.hpp.cmake
rename to cmake/config.hpp.cmake
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-interactive
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-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# HySoP: Hybrid Simulation with Particles\n",
- "\n",
- "HySoP is a library dedicated to high performance flow simulation based on particle methods, on hybrid architectures with multiple compute devices including CPUs, GPUs and MICs.\n",
- "\n",
- "The library is mainly written in Python (high level functionnalities) on the top of Fortran, C++ and OpenCL backends.\n",
- "\n",
- "## How to use HySoP\n",
- "The problem to be solved must be described using Python 2.7 language either interactively or in a python script.\n",
- "\n",
- "If you are not at ease with this language, we strongly encourage you to check one of the numerous python tutorials available on the web. Try for example https://docs.python.org/2/tutorial/. A basic understanding of numpy may also be useful : http://www.numpy.org.\n",
- "Numpy is the python package for scientific computing on which HySoP relies, especially for arrays handling.\n",
- "\n",
- "Interactive session is very useful for tests, basic understanding of hysop functionnalities but real simulation must be executed by using a script. \n",
- "\n",
- "\n",
- "## Quick introduction\n",
- "\n",
- "In this quick introduction we will introduce some of the main data structures and types of HySoP.\n",
- "At the end of this notebook, you will be able to define and discretize Fields on topologies and initialize them.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "%matplotlib inline\n",
- "# First we import HySoP and some types we will need for this tutorial\n",
- "import hysop\n",
- "from hysop import Box, Field, Discretization, CartesianTopology, Simulation\n",
- "\n",
- "# We will also need numpy and matplotlib.\n",
- "import numpy as np\n",
- "np.set_printoptions(linewidth=240, formatter={'float': lambda x: format(x, '6.2f')})\n",
- "\n",
- "import matplotlib.pyplot as plt"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Describing the physical domain\n",
- "The first thing to do is to create a physical domain where the simulation will take place. At this time the only type of domain available are $n$-dimensional boxes. A box has an origin $X_0=(x_0,\\cdots,x_{n-1})$ and a length $L=(L_0, \\cdots, L_{n-1})$. By default a unit box will be returned $X_0 = (0,\\cdots,0)$ and $L=(1,\\cdots,1)$. A domain also provide information about its boundary conditions."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Box::d0 | 2D rectangular box domain:\n",
- " *origin: [ 0.00 0.00]\n",
- " *max_pos: [ 1.00 1.00]\n",
- " *length: [ 1.00 1.00]\n",
- " *left boundary conditions: [PERIODIC(1), PERIODIC(1)]\n",
- " *right boundary conditions: [PERIODIC(1), PERIODIC(1)]\n",
- "\n",
- "d0\n"
- ]
- }
- ],
- "source": [
- "# The 2D default Box\n",
- "domain = Box(dim=2)\n",
- "print domain\n",
- "print domain.tag\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Box::d1 | 2D rectangular box domain:\n",
- " *origin: [ 0.00 0.00]\n",
- " *max_pos: [ 6.28 6.28]\n",
- " *length: [ 6.28 6.28]\n",
- " *left boundary conditions: [PERIODIC(1), PERIODIC(1)]\n",
- " *right boundary conditions: [PERIODIC(1), PERIODIC(1)]\n",
- "\n"
- ]
- }
- ],
- "source": [
- "# A custom box\n",
- "domain = Box(length=(2*np.pi, 2*np.pi))\n",
- "print domain"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "A Box, like many other types in HySoP, has a unique identifier called a tag that identifies any instance of the type uniquely. You can query object tags by using the $tag$ and $full\\_tag$ attributes."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "domain.tag is d1\n",
- "domain.full_tag is Box::d1.\n"
- ]
- }
- ],
- "source": [
- "print 'domain.tag is {}'.format(domain.tag)\n",
- "print 'domain.full_tag is {}.'.format(domain.full_tag)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Of course, a Box has many other usefull attributes describing the physical domain:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "domain.dim is 2.\n",
- "domain.origin is [ 0.00 0.00].\n",
- "domain.length is [ 6.28 6.28].\n",
- "domain.periodicity is [ True True].\n"
- ]
- }
- ],
- "source": [
- "for attr in ('dim','origin','length','periodicity'):\n",
- " print 'domain.{} is {}.'.format(attr, getattr(domain, attr))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "A full list of attributes along with their documentation can be retrieved by using the python help method:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Help on class Box in module hysop.domain.box:\n",
- "\n",
- "class Box(BoxView, hysop.domain.domain.Domain)\n",
- " | Box-shaped domain description.\n",
- " | \n",
- " | Method resolution order:\n",
- " | Box\n",
- " | BoxView\n",
- " | hysop.domain.domain.DomainView\n",
- " | hysop.tools.handle.TaggedObjectView\n",
- " | hysop.domain.domain.Domain\n",
- " | hysop.tools.handle.RegisteredObject\n",
- " | hysop.tools.handle.TaggedObject\n",
- " | __builtin__.object\n",
- " | \n",
- " | Methods defined here:\n",
- " | \n",
- " | view(self, topology_state)\n",
- " | Return a view of this domain altered by some topology_state.\n",
- " | \n",
- " | ----------------------------------------------------------------------\n",
- " | Static methods defined here:\n",
- " | \n",
- " | __new__(cls, length=None, origin=None, dim=None, lboundaries=None, rboundaries=None, **kwds)\n",
- " | Create or get an existing Box from a dimension, length and origin with specified\n",
- " | left and right boundary conditions.\n",
- " | \n",
- " | Parameters\n",
- " | ----------\n",
- " | length : array like of float, optional\n",
- " | Box sides lengthes. Default = [1.0, ...]\n",
- " | origin: array like of float, optional\n",
- " | Position of the lowest point of the box. Default [0.0, ...]\n",
- " | dim: int, optional\n",
- " | Dimension of the box.\n",
- " | lboundaries: array_like of BoundaryCondition\n",
- " | Left boundary conditions.\n",
- " | rboundaries: array_like of BoundaryCondition\n",
- " | Right boundary conditions.\n",
- " | \n",
- " | Attributes\n",
- " | ----------\n",
- " | dim: int\n",
- " | Dimension of the box.\n",
- " | length : np.ndarray of HYSOP_REAL\n",
- " | Box sides lengthes.\n",
- " | origin: np.ndarray of HYSOP_REAL\n",
- " | Position of the lowest point of the box. \n",
- " | end: np.ndarray of HYSOP_REAL\n",
- " | Position of the greatest point of the box. \n",
- " | lboundaries: np.ndarray of BoundaryCondition\n",
- " | Left boundary conditions.\n",
- " | rboundaries: np.ndarray of BoundaryCondition\n",
- " | Right boundary conditions.\n",
- " | boundaries: tuple of np.ndarray of BoundaryCondition\n",
- " | Left and right boundary conditions as a tuple.\n",
- " | periodicity: np.ndarray of bool\n",
- " | Numpy array mask, True is axis is periodic, else False.\n",
- " | \n",
- " | ----------------------------------------------------------------------\n",
- " | Data and other attributes defined here:\n",
- " | \n",
- " | __abstractmethods__ = frozenset([])\n",
- " | \n",
- " | ----------------------------------------------------------------------\n",
- " | Methods inherited from BoxView:\n",
- " | \n",
- " | long_description(self)\n",
- " | Return a long description of this Box as a string.\n",
- " | \n",
- " | short_description(self)\n",
- " | Return a short description of this Box as a string.\n",
- " | \n",
- " | ----------------------------------------------------------------------\n",
- " | Data descriptors inherited from BoxView:\n",
- " | \n",
- " | boundaries\n",
- " | Left and right boundary conditions as a tuple.\n",
- " | \n",
- " | end\n",
- " | Position of the greatest point of the box.\n",
- " | \n",
- " | lboundaries\n",
- " | Left boundary conditions.\n",
- " | \n",
- " | length\n",
- " | Box sides lengthes.\n",
- " | \n",
- " | origin\n",
- " | Position of the lowest point of the box.\n",
- " | \n",
- " | periodicity\n",
- " | Numpy array mask, True is axis is periodic, else False.\n",
- " | \n",
- " | rboundaries\n",
- " | Right boundary conditions.\n",
- " | \n",
- " | ----------------------------------------------------------------------\n",
- " | Methods inherited from hysop.domain.domain.DomainView:\n",
- " | \n",
- " | __eq__(self, other)\n",
- " | \n",
- " | __hash__(self)\n",
- " | \n",
- " | __ne__(self, other)\n",
- " | \n",
- " | __str__(self)\n",
- " | Equivalent to self.long_description()\n",
- " | \n",
- " | current_task(self)\n",
- " | Get task number of the current mpi process.\n",
- " | \n",
- " | is_on_task(self, params)\n",
- " | Test if the current process corresponds to param task.\n",
- " | \n",
- " | print_topologies(self)\n",
- " | Print all topologies registered on the domain.\n",
- " | \n",
- " | task_on_proc(self, parent_rank)\n",
- " | Get task identifier for a given mpi process (parent communicator rank).\n",
- " | \n",
- " | ----------------------------------------------------------------------\n",
- " | Data descriptors inherited from hysop.domain.domain.DomainView:\n",
- " | \n",
- " | dim\n",
- " | Return the dimension of the domain.\n",
- " | \n",
- " | domain\n",
- " | Return the domain on which the view is on.\n",
- " | \n",
- " | frame\n",
- " | Get symbolic frame associated to this domain.\n",
- " | \n",
- " | parent_comm\n",
- " | Return the parent communicator used to create this domain.\n",
- " | \n",
- " | parent_rank\n",
- " | Return the rank of the process in the parent communicator.\n",
- " | \n",
- " | proc_tasks\n",
- " | Return mapping between mpi process rank and task identifier.\n",
- " | \n",
- " | registered_topologies\n",
- " | Return the dictionary of all topologies already built on this domain,\n",
- " | with topology ids as keys and :class:`~hysop.topology.topology.Topology` as values.\n",
- " | \n",
- " | task_comm\n",
- " | Return the communicator that owns the current process.\n",
- " | This is the sub-communicator which has been obtained by splitting.\n",
- " | the parent communicator by colors (proc_tasks).\n",
- " | \n",
- " | task_rank\n",
- " | Return the rank of the process in the task communicator.\n",
- " | \n",
- " | ----------------------------------------------------------------------\n",
- " | Data and other attributes inherited from hysop.domain.domain.DomainView:\n",
- " | \n",
- " | __metaclass__ = <class 'abc.ABCMeta'>\n",
- " | Metaclass for defining Abstract Base Classes (ABCs).\n",
- " | \n",
- " | Use this metaclass to create an ABC. An ABC can be subclassed\n",
- " | directly, and then acts as a mix-in class. You can also register\n",
- " | unrelated concrete classes (even built-in classes) and unrelated\n",
- " | ABCs as 'virtual subclasses' -- these and their descendants will\n",
- " | be considered subclasses of the registering ABC by the built-in\n",
- " | issubclass() function, but the registering ABC won't show up in\n",
- " | their MRO (Method Resolution Order) nor will method\n",
- " | implementations defined by the registering ABC be callable (not\n",
- " | even via super()).\n",
- " | \n",
- " | ----------------------------------------------------------------------\n",
- " | Methods inherited from hysop.tools.handle.TaggedObjectView:\n",
- " | \n",
- " | __init__(self, obj_view=None, **kwds)\n",
- " | \n",
- " | __repr__(self)\n",
- " | \n",
- " | ----------------------------------------------------------------------\n",
- " | Data descriptors inherited from hysop.tools.handle.TaggedObjectView:\n",
- " | \n",
- " | __dict__\n",
- " | dictionary for instance variables (if defined)\n",
- " | \n",
- " | __weakref__\n",
- " | list of weak references to the object (if defined)\n",
- " | \n",
- " | full_pretty_tag\n",
- " | Unique tag of the underlying object view with cls information.\n",
- " | \n",
- " | full_tag\n",
- " | Unique tag of the underlying object view with cls information.\n",
- " | \n",
- " | id\n",
- " | Unique id of the underlying object view.\n",
- " | \n",
- " | pretty_tag\n",
- " | Unique pretty tag of the underlying object view.\n",
- " | \n",
- " | tag\n",
- " | Unique tag of the underlying object view.\n",
- " | \n",
- " | ----------------------------------------------------------------------\n",
- " | Methods inherited from hysop.domain.domain.Domain:\n",
- " | \n",
- " | register_topology(self, topo)\n",
- " | Register a new topology on this domain.\n",
- " | Do nothing if an equivalent topology is already\n",
- " | in the list.\n",
- " | \n",
- " | remove_topology(self, topo)\n",
- " | Remove a topology from the list of this domain.\n",
- " | Do nothing if the topology does not exist in the list.\n",
- " | \n",
- " | ----------------------------------------------------------------------\n",
- " | Methods inherited from hysop.tools.handle.RegisteredObject:\n",
- " | \n",
- " | __del__(self)\n",
- " | \n",
- " | ----------------------------------------------------------------------\n",
- " | Data descriptors inherited from hysop.tools.handle.RegisteredObject:\n",
- " | \n",
- " | obj_initialized\n",
- " | Return the object initialization state.\n",
- "\n"
- ]
- }
- ],
- "source": [
- "help(Box) # or help(domain)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Creating variables\n",
- "\n",
- "Now that we have a domain defined, we can define named continuous scalar and vector fields.\n",
- "\n",
- "In HySoP, a continuous field is an abstract object which represents the usual vector field, in a mathematical sense, i.e some function which associates a scalar or a vector to each point of the space.\n",
- "\n",
- "Such objects are used as input and/or output variables for continuous operators and must be defined with at least:\n",
- "* a name, compulsory (required for i/o).\n",
- "* a domain: the physical domain of definition of the field.\n",
- "\n",
- "The underlying datatype that will be used uppon discretization can also be specified. \n",
- "By default Fields are set to datatype HYSOP_REAL wich will be either np.float32 (single precision floating point) or np.float64 (double precision floating point) depending on HySoP build configuration. There are no restrictions on the datatypes, for example all integer and complex datatypes can also be specified. Let $\\mathbb{D}$ be the space of all the possible values of the chosen datadtype and $\\Omega \\subset \\mathbb{R}^n$ our physical domain.\n",
- "\n",
- "For a domain of dimension $n$, scalar fields $s: \\Omega \\rightarrow \\mathbb{D}$ are defined like this:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "HYSOP_REAL is set to float64.\n",
- "\n",
- "ScalarField::f0\n",
- " *name: F0\n",
- " *pname: F0\n",
- " *dim: 2\n",
- " *dtype: float64\n",
- " *symbolic repr.: F0\n",
- " *initial values: (0, 0)\n",
- " *topology tags: []\n",
- "\n",
- "ScalarField::f1\n",
- " *name: F1\n",
- " *pname: F1\n",
- " *dim: 2\n",
- " *dtype: int32\n",
- " *symbolic repr.: F1\n",
- " *initial values: (0, 0)\n",
- " *topology tags: []\n",
- "\n"
- ]
- }
- ],
- "source": [
- "from hysop.constants import HYSOP_REAL\n",
- "print 'HYSOP_REAL is set to {}.\\n'.format(HYSOP_REAL.__name__)\n",
- "\n",
- "field0 = Field(name='F0', domain=domain)\n",
- "field1 = Field(name='F1', domain=domain, dtype=np.int32)\n",
- "\n",
- "print field0\n",
- "print field1"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Vector fields are created using either the is_vector or the nb_components parameter. is_vector corresponds to specifying nb_components to the number of dimensions of the domain $f(x): \\Omega \\subset \\mathbb{R}^n \\rightarrow \\mathbb{D}^n$ while the more general nb_components parameter allows to specify $g(x): \\Omega \\subset \\mathbb{R}^n \\rightarrow \\mathbb{D}^m$."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "VectorField::tf0\n",
- " *name: V0\n",
- " *pname: V0\n",
- " *dim: 2\n",
- " *shape: (2,)\n",
- " *nb_components: 2\n",
- " *symbolic repr.:\n",
- " [[V0â‚€]\n",
- " [V0â‚]]\n",
- "\n",
- "VectorField::tf1\n",
- " *name: V1\n",
- " *pname: V1\n",
- " *dim: 2\n",
- " *shape: (3,)\n",
- " *nb_components: 3\n",
- " *symbolic repr.:\n",
- " [[V1â‚€]\n",
- " [V1â‚]\n",
- " [V1â‚‚]]\n",
- "\n",
- "TensorField::tf2\n",
- " *name: T\n",
- " *pname: T\n",
- " *dim: 2\n",
- " *shape: (3, 2)\n",
- " *nb_components: 6\n",
- " *symbolic repr.:\n",
- " [[T₀₀ Tâ‚€â‚]\n",
- " [Tâ‚â‚€ Tâ‚â‚]\n",
- " [Tâ‚‚â‚€ Tâ‚‚â‚]]\n"
- ]
- }
- ],
- "source": [
- "field2 = Field(name='V0', domain=domain, is_vector=True)\n",
- "field3 = Field(name='V1', domain=domain, nb_components=3)\n",
- "field4 = Field(name='T', domain=domain, shape=(3,2))\n",
- "\n",
- "print field2\n",
- "print\n",
- "print field3\n",
- "print\n",
- "print field4"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Topologies\n",
- "To discretize Fields we will need to build topologies which are objects use to describe the space discretization of domains. Topologies also defined the mapping between data and MPI processes in case of a multiprocess run.\n",
- "\n",
- "As only Box domains are available at this day, only one type of topology is available as well: CartesianTopology.\n",
- "A cartesian topology represents the discretization of a physical box domain into a cartesian grid with a given space step $dx$. It also is responsible to map subgrids to all processes. This will be called the MPI grid layout. As processes need to exchange data as well, we also need to prescribe a number of ghosts in each directions.\n",
- "\n",
- "Here we will just discretize the domain for one process without ghosts.\n",
- "First we choose the shape of discretization (number of points in each directions).\n",
- "Here we will chose a 5x5 discretization shape. Please note that because of domain periodicity\n",
- "one point will be thrown away in each periodic direction so the effective local grid size will be 4x4."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CartesianTopology::t0[domain=Box::d1, pcoords=[0 0], pshape=[1 1], shape=[5,5], ghosts=[0,0], backend=HOST]\n"
- ]
- }
- ],
- "source": [
- "d0 = Discretization(resolution=(5,5))\n",
- "t0 = CartesianTopology(domain=domain, discretization=d0)\n",
- "print t0.short_description()\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Here pcoords represents the process coordinates into the process grid layout which is of shape (1,1) here.\n",
- "\n",
- "Adding ghosts to the discretization is straightforward:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CartesianTopology::t1[domain=Box::d1, pcoords=[0 0], pshape=[1 1], shape=[5,5], ghosts=[1,2], backend=HOST]\n"
- ]
- }
- ],
- "source": [
- "d1 = Discretization(resolution=(5,5), ghosts=(1,2))\n",
- "t1 = CartesianTopology(domain=domain, discretization=d1)\n",
- "print t1.short_description()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Each CartesianTopology has a 'mesh' attributes, an object of type CartesianMesh, which describes the local grid on which the current mpi process works."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CartesianMeshView::m1:\n",
- " *proc coords: [0 0]\n",
- " *global start: [0 0]\n",
- " *local resolution: [6 8]\n",
- " *compute resolution: [4 4]\n",
- " *ghosts: [1 2]\n",
- " *local boundaries: left => [PERIODIC(1) PERIODIC(1)]\n",
- " right => [PERIODIC(1) PERIODIC(1)]\n",
- "\n"
- ]
- }
- ],
- "source": [
- "print t1.mesh"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Discrete Fields\n",
- "Now that we have topologies defined on domains, we can finally discretize our continuous Fields.\n",
- "Discretizing a Field onto a CartesianTopology yields a CartesianDiscreteField.\n",
- "\n",
- "In fact, the real type that will be returned is a CartesianDiscreteFieldView which is a way to see the underlying discrete field data for a given transposition state. This will come handy when using directionnally splittable operators.\n",
- "The default transposition state is such that the allocated data is C-contiguous. To get the real discrete field on which the view is, just use its 'dfield' attribute.\n",
- "\n",
- "A Field can be at most discretized once per topology:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[[ 0.00 0.00 0.00 0.00]\n",
- " [ 0.00 2.00 2.00 0.00]\n",
- " [ 0.00 0.00 0.00 0.00]\n",
- " [ 0.00 0.00 0.00 0.00]]\n",
- "\n",
- "[[ 1.00 1.00 1.00 1.00]\n",
- " [ 1.00 1.00 1.00 1.00]\n",
- " [ 1.00 1.00 1.00 1.00]\n",
- " [ 1.00 1.00 1.00 1.00]]\n"
- ]
- }
- ],
- "source": [
- "# Here we have a topology without ghosts\n",
- "dfield0 = field0.discretize(t0)\n",
- "dfield0_bis = field0.discretize(t0)\n",
- "assert dfield0.dfield is dfield0_bis.dfield\n",
- "\n",
- "# Accessing data is simple as:\n",
- "dfield0.data[0][...] = 0.0\n",
- "dfield0.data[0][1,1:3] = 2\n",
- "print dfield0.sdata\n",
- "print\n",
- "\n",
- "dfield0.sdata[...] = 1.0\n",
- "print dfield0_bis.sdata"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "All discretized data are of type HostArray, which is a supertype of numpy.ndarray.\n",
- "With the topology with ghost, you will get something like that:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[[ 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00]\n",
- " [ 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00]\n",
- " [ 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00]\n",
- " [ 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00]\n",
- " [ 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00]\n",
- " [ 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00]]\n",
- "\n",
- "[[ nan nan nan nan nan nan nan nan]\n",
- " [ nan nan 1.00 1.00 1.00 1.00 nan nan]\n",
- " [ nan nan 1.00 1.00 1.00 1.00 nan nan]\n",
- " [ nan nan 1.00 1.00 1.00 1.00 nan nan]\n",
- " [ nan nan 1.00 1.00 1.00 1.00 nan nan]\n",
- " [ nan nan nan nan nan nan nan nan]]\n",
- "(slice(1, 5, None), slice(2, 6, None))\n"
- ]
- }
- ],
- "source": [
- "# Here we have a topology with 2 ghosts on the x-axis and 1 ghost in the y-axis\n",
- "dfield1 = field0.discretize(t1)\n",
- "\n",
- "# Put zeros everywhere\n",
- "dfield1.sdata[...] = 0.0\n",
- "print dfield1.sdata\n",
- "print\n",
- "\n",
- "# Put ones inside the compute domain (exluding ghosts)\n",
- "# and NaNs in the ghost layers\n",
- "dfield1.sdata[...] = np.nan\n",
- "dfield1.sdata[dfield1.compute_slices] = 1.0\n",
- "print dfield1.sdata\n",
- "print dfield1.compute_slices"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "There is a simple way to hide ghost values:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[[ X X X X X X X X ]\n",
- " [ X X 1.00 1.00 1.00 1.00 X X ]\n",
- " [ X X 1.00 1.00 1.00 1.00 X X ]\n",
- " [ X X 1.00 1.00 1.00 1.00 X X ]\n",
- " [ X X 1.00 1.00 1.00 1.00 X X ]\n",
- " [ X X X X X X X X ]]\n"
- ]
- }
- ],
- "source": [
- "dfield1.print_with_ghosts(component=0)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How to initialize a scalar field with a python method"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "f(x,y) = x\n",
- "[[ 0.00 1.57 3.14 4.71]\n",
- " [ 0.00 1.57 3.14 4.71]\n",
- " [ 0.00 1.57 3.14 4.71]\n",
- " [ 0.00 1.57 3.14 4.71]]\n",
- "\n",
- "f(x,y) = y\n",
- "[[ 0.00 0.00 0.00 0.00]\n",
- " [ 1.57 1.57 1.57 1.57]\n",
- " [ 3.14 3.14 3.14 3.14]\n",
- " [ 4.71 4.71 4.71 4.71]]\n",
- "\n",
- "f(x,y) = xy\n",
- "[[ 0.00 0.00 0.00 0.00]\n",
- " [ 0.00 2.47 4.93 7.40]\n",
- " [ 0.00 4.93 9.87 14.80]\n",
- " [ 0.00 7.40 14.80 22.21]]\n",
- "\n"
- ]
- }
- ],
- "source": [
- "def f0(data, coords):\n",
- " x,y = coords[0]\n",
- " data[0][...] = x\n",
- " \n",
- "def f1(data, coords):\n",
- " x,y = coords[0]\n",
- " data[0][...] = y\n",
- " \n",
- "def f2(data, coords):\n",
- " x,y = coords[0]\n",
- " data[0][...] = x*y\n",
- "\n",
- "for (f,fname) in zip((f0,f1,f2),('x','y','xy')):\n",
- " dfield0.initialize(f)\n",
- " print 'f(x,y) = {}'.format(fname)\n",
- " print dfield0.sdata\n",
- " print"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How to initialize a vector field with a python method\n",
- "\n",
- "Here we will use print_with_ghosts for a nice formating of field data including ghosts."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Component 0:\n",
- "[[ X X X X X X X X ]\n",
- " [ X X 0.00 1.57 3.14 4.71 X X ]\n",
- " [ X X 0.00 1.57 3.14 4.71 X X ]\n",
- " [ X X 0.00 1.57 3.14 4.71 X X ]\n",
- " [ X X 0.00 1.57 3.14 4.71 X X ]\n",
- " [ X X X X X X X X ]]\n",
- "\n",
- "Component 1:\n",
- "[[ X X X X X X X X ]\n",
- " [ X X 0.00 0.00 0.00 0.00 X X ]\n",
- " [ X X 1.57 1.57 1.57 1.57 X X ]\n",
- " [ X X 3.14 3.14 3.14 3.14 X X ]\n",
- " [ X X 4.71 4.71 4.71 4.71 X X ]\n",
- " [ X X X X X X X X ]]\n",
- "\n",
- "Component 2:\n",
- "[[ X X X X X X X X ]\n",
- " [ X X 0.00 0.00 0.00 0.00 X X ]\n",
- " [ X X 0.00 2.47 4.93 7.40 X X ]\n",
- " [ X X 0.00 4.93 9.87 14.80 X X ]\n",
- " [ X X 0.00 7.40 14.80 22.21 X X ]\n",
- " [ X X X X X X X X ]]\n",
- "\n"
- ]
- }
- ],
- "source": [
- "def f(data, coords):\n",
- " x,y = coords[0]\n",
- " data[0][...] = x\n",
- " data[1][...] = y\n",
- " data[2][...] = x*y\n",
- " \n",
- "dfield3 = field3.discretize(t1) # <- topology with ghosts\n",
- "dfield3.initialize(f)\n",
- "\n",
- "for i in range(dfield3.nb_components):\n",
- " print 'Component {}:'.format(i)\n",
- " dfield3.print_with_ghosts(component=i)\n",
- " print"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How to pass parameters to initialization methods\n",
- "\n",
- "Just pass the additional parameters as keyword arguments to the initialize method."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Component 0:\n",
- "[[ X X X X X X X X ]\n",
- " [ X X 0.00 0.16 0.31 0.47 X X ]\n",
- " [ X X 0.00 0.16 0.31 0.47 X X ]\n",
- " [ X X 0.00 0.16 0.31 0.47 X X ]\n",
- " [ X X 0.00 0.16 0.31 0.47 X X ]\n",
- " [ X X X X X X X X ]]\n",
- "\n",
- "Component 1:\n",
- "[[ X X X X X X X X ]\n",
- " [ X X 0.00 0.00 0.00 0.00 X X ]\n",
- " [ X X 0.16 0.16 0.16 0.16 X X ]\n",
- " [ X X 0.31 0.31 0.31 0.31 X X ]\n",
- " [ X X 0.47 0.47 0.47 0.47 X X ]\n",
- " [ X X X X X X X X ]]\n",
- "\n",
- "Component 2:\n",
- "[[ X X X X X X X X ]\n",
- " [ X X 0.00 0.00 0.00 0.00 X X ]\n",
- " [ X X 0.00 0.25 0.49 0.74 X X ]\n",
- " [ X X 0.00 0.49 0.99 1.48 X X ]\n",
- " [ X X 0.00 0.74 1.48 2.22 X X ]\n",
- " [ X X X X X X X X ]]\n",
- "\n"
- ]
- }
- ],
- "source": [
- "def f(data, coords, p=1.0):\n",
- " x,y = coords[0]\n",
- " data[0][...] = p*x\n",
- " data[1][...] = p*y\n",
- " data[2][...] = p*x*y\n",
- " \n",
- "dfield3 = field3.discretize(t1) # <- topology with ghosts\n",
- "dfield3.initialize(f, p=0.1)\n",
- "\n",
- "for i in range(dfield3.nb_components):\n",
- " print 'Component {}:'.format(i)\n",
- " dfield3.print_with_ghosts(component=i)\n",
- " print"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How to quickly display a scalar or a vector field:\n",
- "This will only display local to process field data."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 1152x720 with 3 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "df = dfield3\n",
- "fig, axes = plt.subplots(1,3, figsize=(16,10))\n",
- "for i in range(df.nb_components):\n",
- " axes[i].imshow(df.data[i][df.compute_slices], \n",
- " interpolation='bicubic',\n",
- " extent=(0.0, 4.71, 4.71, 0))\n",
- "\n",
- " axes[i].set_xlabel('x')\n",
- " axes[i].set_ylabel('y')\n",
- " axes[i].set_title('F{}'.format(i))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Making the fields evolve with time\n",
- "\n",
- "Here we will quickly introduce the Simulation object which will allow us to introduce time in HySoP.\n",
- "By default a simulation begins at t=0 and ends at t=1.\n",
- "The user may supply either a fixed timestep $dt$ or a number of iteration $N$."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [
- {
- "data": {
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- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
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\n",
- "text/plain": [
- "<Figure size 576x360 with 3 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "df = dfield3\n",
- "nb_iter=20\n",
- "simu = Simulation(nb_iter=nb_iter, quiet=True)\n",
- "\n",
- "fig, axes = plt.subplots(1,3, figsize=(8,5))\n",
- "\n",
- "imgs = ()\n",
- "for i in range(df.nb_components):\n",
- " img = axes[i].imshow(df.data[i][df.compute_slices], \n",
- " interpolation='bicubic',\n",
- " extent=(0.0, 4.71, 4.71, 0),\n",
- " vmin=0, vmax=(20 if i==2 else 5))\n",
- " imgs += (img,)\n",
- " axes[i].set_xlabel('x')\n",
- " axes[i].set_ylabel('y')\n",
- " axes[i].set_title('F{}'.format(i))\n",
- "\n",
- "def update():\n",
- " fig.suptitle('t={}'.format(simu.t()))\n",
- " for (i,img) in enumerate(imgs):\n",
- " img.set_data(df.data[i][df.compute_slices])\n",
- " \n",
- "def init():\n",
- " simu.initialize()\n",
- " df.initialize(f, p=1.0, quiet=True)\n",
- " update()\n",
- " return imgs\n",
- "\n",
- "def animate(i):\n",
- " simu.advance()\n",
- " df.initialize(f, p=1.0-simu.t(), quiet=True)\n",
- " update()\n",
- " return imgs\n",
- "\n",
- "from matplotlib import animation\n",
- "from IPython.display import HTML\n",
- "anim = animation.FuncAnimation(fig, animate, init_func=init,\n",
- " frames=nb_iter, interval=200)\n",
- "HTML(anim.to_html5_video())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In the next notebook we will see how to do this the HySoP way by introducing operators and computational graphs."
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 2",
- "language": "python",
- "name": "python2"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 2
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython2",
- "version": "2.7.15+"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/01_analytical.ipynb b/notebooks/01_analytical.ipynb
deleted file mode 100644
index bc75bdef4..000000000
--- a/notebooks/01_analytical.ipynb
+++ /dev/null
@@ -1,249 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# HySoP: Time dependent analytical fields\n",
- "\n",
- "In this notebook we will see how to define analytical fields that evolve with time\n",
- "and how to dump their values to disk at a given dump frequency.\n",
- "\n",
- "* __Level:__ easy\n",
- "* __Recommended lecture:__ 00_introduction notebook\n",
- "\n",
- "In this notebook you will learn how to create, setup and apply operators embedded into a computational graph.\n",
- "\n",
- "Two operators are discussed here:\n",
- "* __PythonAnalyticField:__ update a field with a python method at each simulation step.\n",
- "* __HDF_Writer:__ write a field to disk as an hdf file.\n",
- "\n",
- "## Setting up HySoP\n",
- "\n",
- "Like in the last notebook we need to import the library and \n",
- "required types."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import hysop\n",
- "from hysop.deps import np\n",
- "from hysop import Box, Discretization, CartesianTopology, \\\n",
- " Field, Simulation, Problem\n",
- "from hysop.defaults import TimeParameters\n",
- "from hysop.operators import HDF_Writer\n",
- "from hysop.backend.host.python.operator.analytic import PythonAnalyticField"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Domain and topology\n",
- "First we define a domain, a discretization and finally our topology."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "======== CartesianTopology::t0 ========\n",
- " *on task: 999\n",
- " *backend: HostArrayBackend::bk1\n",
- " *shape: [1 1]\n",
- " *process of coords [0 0] and of ranks cart_rank=0, parent_rank=0\n",
- " *cartesian ranks map:\n",
- " [0]\n",
- " *cartesian to parent comm ranks mapping:\n",
- " [[0]]\n",
- " *neighbours ranks (left, right) x direction \n",
- " [[-1 0]\n",
- " [-1 0]]\n",
- " *BoxView::d0 | 2D rectangular box domain:\n",
- " *origin: [0. 0.]\n",
- " *max_pos: [6.28318531 6.28318531]\n",
- " *length: [6.28318531 6.28318531]\n",
- " *left boundary conditions: [PERIODIC(1), PERIODIC(1)]\n",
- " *right boundary conditions: [PERIODIC(1), PERIODIC(1)]\n",
- " *CartesianMeshView::m0:\n",
- " *proc coords: [0 0]\n",
- " *global start: [0 0]\n",
- " *local resolution: [64 64]\n",
- " *compute resolution: [64 64]\n",
- " *ghosts: [0 0]\n",
- " *local boundaries: left => [PERIODIC(1) PERIODIC(1)]\n",
- " right => [PERIODIC(1) PERIODIC(1)]\n",
- "=================================\n",
- "\n"
- ]
- }
- ],
- "source": [
- "# chose the dimensionality of the domain\n",
- "dim = 2\n",
- "\n",
- "# define the domain\n",
- "box = Box(length=(2*np.pi,)*dim)\n",
- "\n",
- "# discretization parameters (here we choose 64^dim, without ghosts)\n",
- "discretization = Discretization((65,)*dim)\n",
- "\n",
- "# finally create a cartesian topology\n",
- "topo = CartesianTopology(domain=box, discretization=discretization)\n",
- "print topo"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Define the scalar field and time parameters\n",
- "Here we will define the field that will be updated at each time step."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "t,dt = TimeParameters(dtype=np.float32)\n",
- "f0 = Field(name='F0', domain=box, dtype=np.float32)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Define all required operators\n",
- "### Python analytic operator"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "def compute_scalar(data, coords, t):\n",
- " data[0][...] = (1.0/(1.0+0.1*t()))\n",
- " for x in coords[0]:\n",
- " data[0][...] *= np.cos(x-t())\n",
- " \n",
- "# Analytic operator\n",
- "op0 = PythonAnalyticField(name='analytic', field=f0,\n",
- " formula=compute_scalar,\n",
- " variables={f0: topo},\n",
- " extra_input_kwds={'t':t}) # <= here we pass all extra keyword arguments required by formula"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### The HDF writer operator:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "# HDF Writer\n",
- "# by default fields will be dumped to the notebook interactive folder\n",
- "op1 = HDF_Writer(name='dumped_scalar_field', \n",
- " variables={f0:topo})"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "## Build the computational operator graph\n",
- "Build computational operator graph:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Create a problem and build it\n",
- "problem = Problem()\n",
- "problem.insert(op0, op1)\n",
- "problem.build()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Solve and finalize"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Create a simulation and solve the problem \n",
- "# (do not forget to specify the time parameter here)\n",
- "simu = Simulation(start=0.0, end=2*np.pi, \n",
- " nb_iter=100, t=t, dt=dt)\n",
- "\n",
- "# Finally solve the problem \n",
- "problem.solve(simu)\n",
- "\n",
- "# Finalize\n",
- "problem.finalize()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "You can use paraview to visualize the evolution of our scalar field by opening the 'dumped_scalar_field.xmf' file contained in the interactive folder of your current working directory. \n",
- "\n",
- "This is what the result should look like:\n",
- "\n",
- "<video controls src=\"videos/analytic.ogv\" />"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 2",
- "language": "python",
- "name": "python2"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 2
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython2",
- "version": "2.7.15rc1"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/videos/analytic.ogv b/notebooks/videos/analytic.ogv
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diff --git a/setup.py.in b/setup.py.in
index c00757c87..512876d17 100644
--- a/setup.py.in
+++ b/setup.py.in
@@ -18,12 +18,6 @@ import sys
sys.path.append('@CMAKE_BINARY_DIR@/')
import sort_f90
-# --- Check if c++ interface is enabled ---
-enable_cpp = "@USE_CXX@"
-if enable_cpp:
- swig_executable = find_executable("@SWIG_EXECUTABLE@")
-
-
def parseCMakeVar(var, sep=';'):
"""
Post-process cmake list-like variables.
@@ -162,79 +156,6 @@ def create_fortran_extension(name, pyf_file=None, src_dirs=None, sources=None,
return ext_fort
-def create_swig_extension(name, inc_dirs, src_dirs=None, sources=None):
- """Create a python module from C++ files, using swig
-
- Parameters
- ----------
- name: string
- module name
- inc_dirs: list
- list of directories for includes (-I ...), absolute path
- containing headers (.hpp) and swig files (.i)
- sources: list, optional
- all c++ files that must be taken into account
- src_dirs: list, optional
- all directories containing fortran sources (absolute path)
- that must be taken into account. Warning: no recursive scan
- in src_dirs.
-
- Notes
- -----
- * either sources, src_dirs or inc_dirs is required but all
- can be used simultaneously.
- * only .cpp files are taken into account.
- * main config file for swig is assumed to be
- name.i in CMAKE_SOURCE_DIR/swig/
-
- """
- swig_dir = os.path.join('@CMAKE_SOURCE_DIR@', 'swig')
- swig_config_file = os.path.join(swig_dir, name + '.i')
-
- include_dirs = set(inc_dirs)
- if sources is None:
- sources = []
- assert (src_dirs is not None) or (inc_dirs is not None)
- if(src_dirs is None):
- assert(inc_dirs is not None)
- for idir in inc_dirs:
- for root, dirnames, filenames in os.walk(idir):
- for filename in fnmatch.filter(filenames, '*.cpp'):
- sources.append(os.path.join(root, filename))
- else:
- for sdir in src_dirs:
- sources += glob.glob(os.path.join(sdir, '*.cpp'))
-
- sources.insert(0, swig_config_file)
- include_dirs = list(include_dirs)
-
- name = 'hysop._' + name
- swig_opts = ['-I' + swig_dir,
- '-O', '-Wextra', '-Werror',
- '-c++', '-extranative', '-safecstrings']
-
- extern_includes = parseCMakeVar("@CXX_EXT_INCLUDES@")
- for exti in extern_includes:
- include_dirs.append(exti)
-
- libraries = parseCMakeVar("@CXX_EXT_LIBS@")
- library_dirs = parseCMakeVar("@CXX_EXT_LIB_DIRS@")
- extra_compile_args = parseCMakeVar("@CXX_FLAGS@", sep=' ')
- extra_link_args = parseCMakeVar("@CXX_LINKER_FLAGS@", sep=' ')
- define_macros = parseCMakeDefines("@CXX_EXTRA_DEFINES@")
- swig_ext = Extension(name, sources=sources, language='c++',
- swig_opts=swig_opts,
- include_dirs=include_dirs,
- library_dirs=library_dirs,
- libraries=libraries,
- define_macros=define_macros,
- runtime_library_dirs=library_dirs,
- extra_compile_args=extra_compile_args,
- extra_link_args=extra_link_args)
-
- return swig_ext
-
-
# ------------ Set list of packages required to build the module -------------
# List of modules (directories) to be included
with_test = "@WITH_TESTS@" == "ON"
@@ -324,28 +245,10 @@ if enable_fortran == "ON":
for ex in ext:
ext_modules.append(ext[ex])
-# --- C++ files and swig interface --
-
-if enable_cpp == "ON":
- # path to .i files
- swig_include_dirs = [os.path.join('@CMAKE_SOURCE_DIR@','swig')]
-
- cpp_include_dirs = ['src/fftw','src/hysop++/src']
- for d in cpp_include_dirs:
- swig_include_dirs.append(os.path.join('@CMAKE_SOURCE_DIR@', d))
-
- ext = {}
- cpp2hysop = "@CPP_2_HYSOP@"
- ext[cpp2hysop] = create_swig_extension(name=cpp2hysop,
- inc_dirs=swig_include_dirs)
- for ex in ext:
- ext_modules.append(ext[ex])
-
data_files = []
descr = 'Hybrid Computation with Particles.'
authors = 'G.H Cottet, J.M Etancelin, J.B Keck, C.Mimeau, F.Pérignon, C. Picard'
-# authors = 'HySoP development team'
config = Configuration(
name='@PACKAGE_NAME@',
version='@HYSOP_VERSION@',
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index a3d17a69c..45641959b 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -48,8 +48,8 @@ set(${HYSOP_LIBRARY_NAME}_HDRS ${HDRS_FILES})
# ============= Generates HySoPConfig.hpp =============
# The file HYSOP_LIBRARY_NAME_defines.hpp will be generated from config.hpp.cmake;
-if(EXISTS ${CMAKE_SOURCE_DIR}/config.hpp.cmake)
- configure_file(${CMAKE_SOURCE_DIR}/config.hpp.cmake ${HYSOP_LIBRARY_NAME}_defines.hpp)
+if(EXISTS ${CMAKE_SOURCE_DIR}/cmake/config.hpp.cmake)
+ configure_file(${CMAKE_SOURCE_DIR}/cmake/config.hpp.cmake ${HYSOP_LIBRARY_NAME}_defines.hpp)
include_directories(${CMAKE_BINARY_DIR})
endif()
@@ -67,34 +67,3 @@ include_directories(${HYSOP_INCLUDE_DIRECTORIES})
add_library(${HYSOP_LIBRARY_NAME} STATIC ${${HYSOP_LIBRARY_NAME}_SRCS})
target_link_libraries(${HYSOP_LIBRARY_NAME} PRIVATE ${HYSOP_LINK_LIBRARIES})
-
-#install(TARGETS ${HYSOP_LIBRARY_NAME}
-# LIBRARY DESTINATION ${HYSOP_PYTHON_INSTALL_DIR}/${PACKAGE_NAME})
-
-# ============= Create an executable linked with libhysop =============
-# This part is optional and only useful to test libhysop in a
-# way that does not depends on python.
-# At the time it only includes fftw tests.
-if(WITH_FORTRAN_TESTS)
- # if(WITH_FFTW)
- # # Set the name of a executable file that will be linked with libHYSOP_LIBRARY_NAME.
- # # Useful to test libhysop in a way that does not depend on python.
- # set(EXE_NAME ${HYSOP_LIBRARY_NAME}Run)
- # # A main file to create an executable (test purpose)
- # # Any files in these dirs will be used to create HySoP exec (linked with libhysop)
- # set(${EXE_NAME}_SRCDIRS main)
- # # Source and header files list, to generate a working executable based on libhysop.
- # get_sources("${${EXE_NAME}_SRCDIRS}")
- # get_headers("${${EXE_NAME}_SRCDIRS}")
- # set(${EXE_NAME}_SRCS ${SOURCES_FILES})
- # set(${EXE_NAME}_HDRS ${HDRS_FILES})
- # list(APPEND ${EXE_NAME}_SRC ${${EXE_NAME}_HDRS})
- # include_directories(${${EXE_NAME}_HDRS})
- # add_executable(${EXE_NAME} ${${EXE_NAME}_SRCS})
- # add_dependencies(${EXE_NAME} ${HYSOP_LIBRARY_NAME})
-
- # # libs to link with EXE_NAME
- # target_link_libraries(${EXE_NAME} ${HYSOP_LIBRARY_NAME})
- # target_link_libraries(${EXE_NAME} ${HYSOP_LINK_LIBRARIES})
- # endif()
-endif()
diff --git a/swig/cpp2hysop.i b/swig/cpp2hysop.i
deleted file mode 100644
index 7b1e2deb2..000000000
--- a/swig/cpp2hysop.i
+++ /dev/null
@@ -1,7 +0,0 @@
-// -*- C++ -*-
-%module cpp2hysop
-
-%include start.i
-
-%include hysop++/hysop++.i
-
diff --git a/swig/hysop++/domain.i b/swig/hysop++/domain.i
deleted file mode 100644
index 8e996563f..000000000
--- a/swig/hysop++/domain.i
+++ /dev/null
@@ -1,38 +0,0 @@
-%module domain
-
-// -*- C++ -*-
-%{
- #include "domain/boundary.h"
- #include "domain/domainConfiguration.h"
-%}
-
-/* domain boundary */
-%ignore hysop::domain::operator<<;
-%include "domain/boundary.h"
-
-
-/* domain configuration */
-%ignore hysop::domain::operator<<;
-%ignore hysop::domain::DomainConfiguration::operator[];
-%include "domain/domainConfiguration.h"
-
-%define INSTANTIATE_DOMAIN_CONFIG_IMPL(CLASS_NAME,CLASS_TEMPLATE)
- %template(CLASS_NAME) CLASS_TEMPLATE;
- %extend CLASS_TEMPLATE {
- CLASS_TEMPLATE::BoundaryPair __get_item__(unsigned int k) const {
- return $self->operator[](k);
- }
- const char* __str__() const {
- std::stringstream ss;
- ss << *($self);
- return ss.str().c_str();
- }
- }
-%enddef
-
-%define INSTANTIATE_DOMAIN_CONFIG(INT)
- INSTANTIATE_DOMAIN_CONFIG_IMPL(DomainConfiguration ## INT ## D, hysop::domain::DomainConfiguration<INT>)
-%enddef
-
-%formacro(INSTANTIATE_DOMAIN_CONFIG, INSTANTIATED_DIMENSIONS)
-
diff --git a/swig/hysop++/fft.i b/swig/hysop++/fft.i
deleted file mode 100644
index dd62b7686..000000000
--- a/swig/hysop++/fft.i
+++ /dev/null
@@ -1,94 +0,0 @@
-
-%module fft
-
-// -*- C++ -*-
-%{
- #include "fftw3.h"
- #include "fft/fftw3.h"
- #include "fft/extension.h"
- #include "fft/transform.h"
- #include "fft/fftDomainConfiguration.h"
-%}
-
-/* fftw3.h */
-#if !defined(FFTW_HAS_FFTW3F_THREADS) || !defined(FFTW_HAS_FFTW3F_OMP)
- %ignore fftwf_init_threads();
- %ignore fftwf_plan_with_nthreads(int);
- %ignore fftwf_make_planner_thread_safe();
- %ignore fftwf_cleanup_threads();
-#endif
-
-#if !defined(FFTW_HAS_FFTW3D_THREADS) || !defined(FFTW_HAS_FFTW3D_OMP)
- %ignore fftw_init_threads();
- %ignore fftw_plan_with_nthreads(int);
- %ignore fftw_make_planner_thread_safe();
- %ignore fftw_cleanup_threads();
-#endif
-
-#if !defined(FFTW_HAS_FFTW3L_THREADS) || !defined(FFTW_HAS_FFTW3L_OMP)
- %ignore fftwl_init_threads();
- %ignore fftwl_plan_with_nthreads(int);
- %ignore fftwl_make_planner_thread_safe();
- %ignore fftwl_cleanup_threads();
-#endif
-
-#if !defined(FFTW_HAS_FFTW3Q_THREADS) || !defined(FFTW_HAS_FFTW3Q_OMP)
- %ignore fftwq_init_threads();
- %ignore fftwq_plan_with_nthreads(int);
- %ignore fftwq_make_planner_thread_safe();
- %ignore fftwq_cleanup_threads();
-#endif
-
-%rename(is_) fftw_iodim_do_not_use_me::is;
-%rename(is_) fftw_iodim64_do_not_use_me::is;
-%include "fftw3.h"
-
-/* fftw3 c++ wrappers */
-%include "fft/fftw3.h"
-%template(Fftw3f) hysop::fft::Fftw3<float>;
-%template(Fftw3d) hysop::fft::Fftw3<double>;
-%template(Fftw3l) hysop::fft::Fftw3<long double>;
-#ifdef HAS_QUADMATHS
- %template(Fftw3q) hysop::fft::Fftw3<__float128>;
-#endif
-
-/* fft transforms */
-%ignore hysop::fft::operator<<;
-%include "fft/transform.h"
-%extend hysop::fft::Transform {
- const char* __str__() const {
- return $self->toString().c_str();
- }
-}
-
-
-/* fft extensions */
-%ignore hysop::fft::operator<<;
-%include "fft/extension.h"
-
-
-/* fft domain configuration */
-%ignore hysop::fft::operator<<;
-%ignore hysop::fft::FftDomainConfiguration::operator[];
-%include "fft/fftDomainConfiguration.h"
-
-%define INSTANTIATE_FFT_DOMAIN_CONFIG_IMPL(CLASS_NAME,CLASS_TEMPLATE...)
- %template(CLASS_NAME) CLASS_TEMPLATE;
- %extend CLASS_TEMPLATE {
- CLASS_TEMPLATE::ExtensionPair __get_item__(unsigned int k) const {
- return $self->operator[](k);
- }
- const char* __str__() const {
- std::stringstream ss;
- ss << *($self);
- return ss.str().c_str();
- }
- }
-%enddef
-
-%define INSTANTIATE_FFT_DOMAIN_CONFIG(INT)
- INSTANTIATE_FFT_DOMAIN_CONFIG_IMPL(FftDomainConfiguration ## INT ## D, hysop::fft::FftDomainConfiguration<INT>)
-%enddef
-
-%formacro(INSTANTIATE_FFT_DOMAIN_CONFIG, INSTANTIATED_DIMENSIONS)
-
diff --git a/swig/hysop++/hysop++.i b/swig/hysop++/hysop++.i
deleted file mode 100644
index a681efa69..000000000
--- a/swig/hysop++/hysop++.i
+++ /dev/null
@@ -1,5 +0,0 @@
-
-/* hysop++ */
-%include "utils.i"
-%include "domain.i"
-%include "fft.i"
diff --git a/swig/hysop++/utils.i b/swig/hysop++/utils.i
deleted file mode 100644
index 541179ea9..000000000
--- a/swig/hysop++/utils.i
+++ /dev/null
@@ -1,29 +0,0 @@
-
-%module utils
-
-// -*- C++ -*-
-%{
- #include "utils/types.h"
- #include "utils/constants.h"
-%}
-
-/* types */
-%warnfilter(342) hysop::multi_array;
-%warnfilter(342) hysop::multi_array_view;
-%warnfilter(342) hysop::const_multi_array_view;
-%warnfilter(342) hysop::multi_array_ref;
-%warnfilter(342) hysop::const_multi_array_ref;
-%ignore hysop::multi_array;
-%ignore hysop::multi_array_view;
-%ignore hysop::const_multi_array_view;
-%ignore hysop::multi_array_ref;
-%ignore hysop::const_multi_array_ref;
-%include "utils/types.h"
-
-%define INSTANTIATE_SHAPE(INT) %template(Shape ## INT ## D) hysop::Shape<INT>; %enddef
-%define INSTANTIATE_OFFSET(INT) %template(Offset ## INT ## D) hysop::Offset<INT>; %enddef
-%formacro(INSTANTIATE_SHAPE, INSTANTIATED_DIMENSIONS)
-%formacro(INSTANTIATE_OFFSET, INSTANTIATED_DIMENSIONS)
-
-/* constants */
-%include "utils/constants.h"
diff --git a/swig/numpy.i b/swig/numpy.i
deleted file mode 100644
index d3eb0c218..000000000
--- a/swig/numpy.i
+++ /dev/null
@@ -1,3085 +0,0 @@
-/* -*- C -*- (not really, but good for syntax highlighting) */
-#ifdef SWIGPYTHON
-
-%{
-#ifndef SWIG_FILE_WITH_INIT
-#define NO_IMPORT_ARRAY
-#endif
-#include "stdio.h"
-#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
-#include <numpy/arrayobject.h>
-%}
-
-/**********************************************************************/
-
-%fragment("NumPy_Backward_Compatibility", "header")
-{
-%#if NPY_API_VERSION < 0x00000007
-%#define NPY_ARRAY_DEFAULT NPY_DEFAULT
-%#define NPY_ARRAY_FARRAY NPY_FARRAY
-%#define NPY_ARRAY_F_CONTIGUOUS NPY_F_CONTIGUOUS
-%#endif
-}
-
-/**********************************************************************/
-
-/* The following code originally appeared in
- * enthought/kiva/agg/src/numeric.i written by Eric Jones. It was
- * translated from C++ to C by John Hunter. Bill Spotz has modified
- * it to fix some minor bugs, upgrade from Numeric to numpy (all
- * versions), add some comments and functionality, and convert from
- * direct code insertion to SWIG fragments.
- */
-
-%fragment("NumPy_Macros", "header")
-{
-/* Macros to extract array attributes.
- */
-%#if NPY_API_VERSION < 0x00000007
-%#define is_array(a) ((a) && PyArray_Check((PyArrayObject*)a))
-%#define array_type(a) (int)(PyArray_TYPE((PyArrayObject*)a))
-%#define array_numdims(a) (((PyArrayObject*)a)->nd)
-%#define array_dimensions(a) (((PyArrayObject*)a)->dimensions)
-%#define array_size(a,i) (((PyArrayObject*)a)->dimensions[i])
-%#define array_strides(a) (((PyArrayObject*)a)->strides)
-%#define array_stride(a,i) (((PyArrayObject*)a)->strides[i])
-%#define array_data(a) (((PyArrayObject*)a)->data)
-%#define array_descr(a) (((PyArrayObject*)a)->descr)
-%#define array_flags(a) (((PyArrayObject*)a)->flags)
-%#define array_enableflags(a,f) (((PyArrayObject*)a)->flags) = f
-%#else
-%#define is_array(a) ((a) && PyArray_Check(a))
-%#define array_type(a) PyArray_TYPE((PyArrayObject*)a)
-%#define array_numdims(a) PyArray_NDIM((PyArrayObject*)a)
-%#define array_dimensions(a) PyArray_DIMS((PyArrayObject*)a)
-%#define array_strides(a) PyArray_STRIDES((PyArrayObject*)a)
-%#define array_stride(a,i) PyArray_STRIDE((PyArrayObject*)a,i)
-%#define array_size(a,i) PyArray_DIM((PyArrayObject*)a,i)
-%#define array_data(a) PyArray_DATA((PyArrayObject*)a)
-%#define array_descr(a) PyArray_DESCR((PyArrayObject*)a)
-%#define array_flags(a) PyArray_FLAGS((PyArrayObject*)a)
-%#define array_enableflags(a,f) PyArray_ENABLEFLAGS((PyArrayObject*)a,f)
-%#endif
-%#define array_is_contiguous(a) (PyArray_ISCONTIGUOUS((PyArrayObject*)a))
-%#define array_is_native(a) (PyArray_ISNOTSWAPPED((PyArrayObject*)a))
-%#define array_is_fortran(a) (PyArray_ISFORTRAN((PyArrayObject*)a))
-}
-
-/**********************************************************************/
-
-%fragment("NumPy_Utilities",
- "header")
-{
- /* Given a PyObject, return a string describing its type.
- */
- const char* pytype_string(PyObject* py_obj)
- {
- if (py_obj == NULL ) return "C NULL value";
- if (py_obj == Py_None ) return "Python None" ;
- if (PyCallable_Check(py_obj)) return "callable" ;
- if (PyString_Check( py_obj)) return "string" ;
- if (PyInt_Check( py_obj)) return "int" ;
- if (PyFloat_Check( py_obj)) return "float" ;
- if (PyDict_Check( py_obj)) return "dict" ;
- if (PyList_Check( py_obj)) return "list" ;
- if (PyTuple_Check( py_obj)) return "tuple" ;
-%#if PY_MAJOR_VERSION < 3
- if (PyFile_Check( py_obj)) return "file" ;
- if (PyModule_Check( py_obj)) return "module" ;
- if (PyInstance_Check(py_obj)) return "instance" ;
-%#endif
-
- return "unkown type";
- }
-
- /* Given a NumPy typecode, return a string describing the type.
- */
- const char* typecode_string(int typecode)
- {
- static const char* type_names[25] = {"bool",
- "byte",
- "unsigned byte",
- "short",
- "unsigned short",
- "int",
- "unsigned int",
- "long",
- "unsigned long",
- "long long",
- "unsigned long long",
- "float",
- "double",
- "long double",
- "complex float",
- "complex double",
- "complex long double",
- "object",
- "string",
- "unicode",
- "void",
- "ntypes",
- "notype",
- "char",
- "unknown"};
- return typecode < 24 ? type_names[typecode] : type_names[24];
- }
-
- /* Make sure input has correct numpy type. This now just calls
- PyArray_EquivTypenums().
- */
- int type_match(int actual_type,
- int desired_type)
- {
- return PyArray_EquivTypenums(actual_type, desired_type);
- }
-
-%#ifdef SWIGPY_USE_CAPSULE
- void free_cap(PyObject * cap)
- {
- void* array = (void*) PyCapsule_GetPointer(cap,SWIGPY_CAPSULE_NAME);
- if (array != NULL) free(array);
- }
-%#endif
-
-
-}
-
-/**********************************************************************/
-
-%fragment("NumPy_Object_to_Array",
- "header",
- fragment="NumPy_Backward_Compatibility",
- fragment="NumPy_Macros",
- fragment="NumPy_Utilities")
-{
- /* Given a PyObject pointer, cast it to a PyArrayObject pointer if
- * legal. If not, set the python error string appropriately and
- * return NULL.
- */
- PyArrayObject* obj_to_array_no_conversion(PyObject* input,
- int typecode)
- {
- PyArrayObject* ary = NULL;
- if (is_array(input) && (typecode == NPY_NOTYPE ||
- PyArray_EquivTypenums(array_type(input), typecode)))
- {
- ary = (PyArrayObject*) input;
- }
- else if is_array(input)
- {
- const char* desired_type = typecode_string(typecode);
- const char* actual_type = typecode_string(array_type(input));
- PyErr_Format(PyExc_TypeError,
- "Array of type '%s' required. Array of type '%s' given",
- desired_type, actual_type);
- ary = NULL;
- }
- else
- {
- const char* desired_type = typecode_string(typecode);
- const char* actual_type = pytype_string(input);
- PyErr_Format(PyExc_TypeError,
- "Array of type '%s' required. A '%s' was given",
- desired_type,
- actual_type);
- ary = NULL;
- }
- return ary;
- }
-
- /* Convert the given PyObject to a NumPy array with the given
- * typecode. On success, return a valid PyArrayObject* with the
- * correct type. On failure, the python error string will be set and
- * the routine returns NULL.
- */
- PyArrayObject* obj_to_array_allow_conversion(PyObject* input,
- int typecode,
- int* is_new_object)
- {
- PyArrayObject* ary = NULL;
- PyObject* py_obj;
- if (is_array(input) && (typecode == NPY_NOTYPE ||
- PyArray_EquivTypenums(array_type(input),typecode)))
- {
- ary = (PyArrayObject*) input;
- *is_new_object = 0;
- }
- else
- {
- py_obj = PyArray_FROMANY(input, typecode, 0, 0, NPY_ARRAY_DEFAULT);
- /* If NULL, PyArray_FromObject will have set python error value.*/
- ary = (PyArrayObject*) py_obj;
- *is_new_object = 1;
- }
- return ary;
- }
-
- /* Given a PyArrayObject, check to see if it is contiguous. If so,
- * return the input pointer and flag it as not a new object. If it is
- * not contiguous, create a new PyArrayObject using the original data,
- * flag it as a new object and return the pointer.
- */
- PyArrayObject* make_contiguous(PyArrayObject* ary,
- int* is_new_object,
- int min_dims,
- int max_dims)
- {
- PyArrayObject* result;
- if (array_is_contiguous(ary))
- {
- result = ary;
- *is_new_object = 0;
- }
- else
- {
- result = (PyArrayObject*) PyArray_ContiguousFromObject((PyObject*)ary,
- array_type(ary),
- min_dims,
- max_dims);
- *is_new_object = 1;
- }
- return result;
- }
-
- /* Given a PyArrayObject, check to see if it is Fortran-contiguous.
- * If so, return the input pointer, but do not flag it as not a new
- * object. If it is not Fortran-contiguous, create a new
- * PyArrayObject using the original data, flag it as a new object
- * and return the pointer.
- */
- PyArrayObject* make_fortran(PyArrayObject* ary,
- int* is_new_object)
- {
- PyArrayObject* result;
- if (array_is_fortran(ary))
- {
- result = ary;
- *is_new_object = 0;
- }
- else
- {
- Py_INCREF(array_descr(ary));
- result = (PyArrayObject*) PyArray_FromArray(ary,
- array_descr(ary),
- NPY_ARRAY_F_CONTIGUOUS);
- *is_new_object = 1;
- }
- return result;
- }
-
- /* Convert a given PyObject to a contiguous PyArrayObject of the
- * specified type. If the input object is not a contiguous
- * PyArrayObject, a new one will be created and the new object flag
- * will be set.
- */
- PyArrayObject* obj_to_array_contiguous_allow_conversion(PyObject* input,
- int typecode,
- int* is_new_object)
- {
- int is_new1 = 0;
- int is_new2 = 0;
- PyArrayObject* ary2;
- PyArrayObject* ary1 = obj_to_array_allow_conversion(input,
- typecode,
- &is_new1);
- if (ary1)
- {
- ary2 = make_contiguous(ary1, &is_new2, 0, 0);
- if ( is_new1 && is_new2)
- {
- Py_DECREF(ary1);
- }
- ary1 = ary2;
- }
- *is_new_object = is_new1 || is_new2;
- return ary1;
- }
-
- /* Convert a given PyObject to a Fortran-ordered PyArrayObject of the
- * specified type. If the input object is not a Fortran-ordered
- * PyArrayObject, a new one will be created and the new object flag
- * will be set.
- */
- PyArrayObject* obj_to_array_fortran_allow_conversion(PyObject* input,
- int typecode,
- int* is_new_object)
- {
- int is_new1 = 0;
- int is_new2 = 0;
- PyArrayObject* ary2;
- PyArrayObject* ary1 = obj_to_array_allow_conversion(input,
- typecode,
- &is_new1);
- if (ary1)
- {
- ary2 = make_fortran(ary1, &is_new2);
- if (is_new1 && is_new2)
- {
- Py_DECREF(ary1);
- }
- ary1 = ary2;
- }
- *is_new_object = is_new1 || is_new2;
- return ary1;
- }
-} /* end fragment */
-
-/**********************************************************************/
-
-%fragment("NumPy_Array_Requirements",
- "header",
- fragment="NumPy_Backward_Compatibility",
- fragment="NumPy_Macros")
-{
- /* Test whether a python object is contiguous. If array is
- * contiguous, return 1. Otherwise, set the python error string and
- * return 0.
- */
- int require_contiguous(PyArrayObject* ary)
- {
- int contiguous = 1;
- if (!array_is_contiguous(ary))
- {
- PyErr_SetString(PyExc_TypeError,
- "Array must be contiguous. A non-contiguous array was given");
- contiguous = 0;
- }
- return contiguous;
- }
-
- /* Require that a numpy array is not byte-swapped. If the array is
- * not byte-swapped, return 1. Otherwise, set the python error string
- * and return 0.
- */
- int require_native(PyArrayObject* ary)
- {
- int native = 1;
- if (!array_is_native(ary))
- {
- PyErr_SetString(PyExc_TypeError,
- "Array must have native byteorder. "
- "A byte-swapped array was given");
- native = 0;
- }
- return native;
- }
-
- /* Require the given PyArrayObject to have a specified number of
- * dimensions. If the array has the specified number of dimensions,
- * return 1. Otherwise, set the python error string and return 0.
- */
- int require_dimensions(PyArrayObject* ary,
- int exact_dimensions)
- {
- int success = 1;
- if (array_numdims(ary) != exact_dimensions)
- {
- PyErr_Format(PyExc_TypeError,
- "Array must have %d dimensions. Given array has %d dimensions",
- exact_dimensions,
- array_numdims(ary));
- success = 0;
- }
- return success;
- }
-
- /* Require the given PyArrayObject to have one of a list of specified
- * number of dimensions. If the array has one of the specified number
- * of dimensions, return 1. Otherwise, set the python error string
- * and return 0.
- */
- int require_dimensions_n(PyArrayObject* ary,
- int* exact_dimensions,
- int n)
- {
- int success = 0;
- int i = 0;
- char dims_str[255] = "";
- char s[255];
- for (i = 0; i < n && !success; i++)
- {
- if (array_numdims(ary) == exact_dimensions[i])
- {
- success = 1;
- }
- }
- if (!success)
- {
- for (i = 0; i < n-1; i++)
- {
- sprintf(s, "%d, ", exact_dimensions[i]);
- strcat(dims_str,s);
- }
- sprintf(s, " or %d", exact_dimensions[n-1]);
- strcat(dims_str,s);
- PyErr_Format(PyExc_TypeError,
- "Array must have %s dimensions. Given array has %d dimensions",
- dims_str,
- array_numdims(ary));
- }
- return success;
- }
-
- /* Require the given PyArrayObject to have a specified shape. If the
- * array has the specified shape, return 1. Otherwise, set the python
- * error string and return 0.
- */
- int require_size(PyArrayObject* ary,
- npy_intp* size,
- int n)
- {
- int i = 0;
- int success = 1;
- int len = 0;
- char desired_dims[255] = "[";
- char s[255];
- char actual_dims[255] = "[";
- for(i=0; i < n;i++)
- {
- if (size[i] != -1 && size[i] != array_size(ary,i))
- {
- success = 0;
- }
- }
- if (!success)
- {
- for (i = 0; i < n; i++)
- {
- if (size[i] == -1)
- {
- sprintf(s, "*,");
- }
- else
- {
- sprintf(s, "%ld,", (long int)size[i]);
- }
- strcat(desired_dims,s);
- }
- len = strlen(desired_dims);
- desired_dims[len-1] = ']';
- for (i = 0; i < n; i++)
- {
- sprintf(s, "%ld,", (long int)array_size(ary,i));
- strcat(actual_dims,s);
- }
- len = strlen(actual_dims);
- actual_dims[len-1] = ']';
- PyErr_Format(PyExc_TypeError,
- "Array must have shape of %s. Given array has shape of %s",
- desired_dims,
- actual_dims);
- }
- return success;
- }
-
- /* Require the given PyArrayObject to to be Fortran ordered. If the
- * the PyArrayObject is already Fortran ordered, do nothing. Else,
- * set the Fortran ordering flag and recompute the strides.
- */
- int require_fortran(PyArrayObject* ary)
- {
- int success = 1;
- int nd = array_numdims(ary);
- int i = 0;
- npy_intp * strides = array_strides(ary);
- if (array_is_fortran(ary)) return success;
- /* Set the Fortran ordered flag */
- array_enableflags(ary,NPY_ARRAY_FARRAY);
- /* Recompute the strides */
- strides[0] = strides[nd-1];
- for (i=1; i < nd; ++i)
- strides[i] = strides[i-1] * array_size(ary,i-1);
- return success;
- }
-}
-
-/* Combine all NumPy fragments into one for convenience */
-%fragment("NumPy_Fragments",
- "header",
- fragment="NumPy_Backward_Compatibility",
- fragment="NumPy_Macros",
- fragment="NumPy_Utilities",
- fragment="NumPy_Object_to_Array",
- fragment="NumPy_Array_Requirements")
-{
-}
-
-/* End John Hunter translation (with modifications by Bill Spotz)
- */
-
-/* %numpy_typemaps() macro
- *
- * This macro defines a family of 74 typemaps that allow C arguments
- * of the form
- *
- * 1. (DATA_TYPE IN_ARRAY1[ANY])
- * 2. (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1)
- * 3. (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1)
- *
- * 4. (DATA_TYPE IN_ARRAY2[ANY][ANY])
- * 5. (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
- * 6. (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2)
- * 7. (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
- * 8. (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2)
- *
- * 9. (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY])
- * 10. (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
- * 11. (DATA_TYPE** IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
- * 12. (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3)
- * 13. (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
- * 14. (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_FARRAY3)
- *
- * 15. (DATA_TYPE IN_ARRAY4[ANY][ANY][ANY][ANY])
- * 16. (DATA_TYPE* IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
- * 17. (DATA_TYPE** IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
- * 18. (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, , DIM_TYPE DIM4, DATA_TYPE* IN_ARRAY4)
- * 19. (DATA_TYPE* IN_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
- * 20. (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* IN_FARRAY4)
- *
- * 21. (DATA_TYPE INPLACE_ARRAY1[ANY])
- * 22. (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1)
- * 23. (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1)
- *
- * 24. (DATA_TYPE INPLACE_ARRAY2[ANY][ANY])
- * 25. (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
- * 26. (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2)
- * 27. (DATA_TYPE* INPLACE_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
- * 28. (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_FARRAY2)
- *
- * 29. (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY])
- * 30. (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
- * 31. (DATA_TYPE** INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
- * 32. (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_ARRAY3)
- * 33. (DATA_TYPE* INPLACE_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
- * 34. (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_FARRAY3)
- *
- * 35. (DATA_TYPE INPLACE_ARRAY4[ANY][ANY][ANY][ANY])
- * 36. (DATA_TYPE* INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
- * 37. (DATA_TYPE** INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
- * 38. (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* INPLACE_ARRAY4)
- * 39. (DATA_TYPE* INPLACE_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
- * 40. (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* INPLACE_FARRAY4)
- *
- * 41. (DATA_TYPE ARGOUT_ARRAY1[ANY])
- * 42. (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1)
- * 43. (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1)
- *
- * 44. (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY])
- *
- * 45. (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY])
- *
- * 46. (DATA_TYPE ARGOUT_ARRAY4[ANY][ANY][ANY][ANY])
- *
- * 47. (DATA_TYPE** ARGOUTVIEW_ARRAY1, DIM_TYPE* DIM1)
- * 48. (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEW_ARRAY1)
- *
- * 49. (DATA_TYPE** ARGOUTVIEW_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2)
- * 50. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_ARRAY2)
- * 51. (DATA_TYPE** ARGOUTVIEW_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2)
- * 52. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_FARRAY2)
- *
- * 53. (DATA_TYPE** ARGOUTVIEW_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3)
- * 54. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_ARRAY3)
- * 55. (DATA_TYPE** ARGOUTVIEW_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3)
- * 56. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_FARRAY3)
- *
- * 57. (DATA_TYPE** ARGOUTVIEW_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4)
- * 58. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEW_ARRAY4)
- * 59. (DATA_TYPE** ARGOUTVIEW_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4)
- * 60. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEW_FARRAY4)
- *
- * 61. (DATA_TYPE** ARGOUTVIEWM_ARRAY1, DIM_TYPE* DIM1)
- * 62. (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEWM_ARRAY1)
- *
- * 63. (DATA_TYPE** ARGOUTVIEWM_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2)
- * 64. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEWM_ARRAY2)
- * 65. (DATA_TYPE** ARGOUTVIEWM_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2)
- * 66. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEWM_FARRAY2)
- *
- * 67. (DATA_TYPE** ARGOUTVIEWM_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3)
- * 68. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEWM_ARRAY3)
- * 69. (DATA_TYPE** ARGOUTVIEWM_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3)
- * 70. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEWM_FARRAY3)
- *
- * 71. (DATA_TYPE** ARGOUTVIEWM_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4)
- * 72. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_ARRAY4)
- * 73. (DATA_TYPE** ARGOUTVIEWM_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4)
- * 74. (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_FARRAY4)
- *
- * where "DATA_TYPE" is any type supported by the NumPy module, and
- * "DIM_TYPE" is any int-like type suitable for specifying dimensions.
- * The difference between "ARRAY" typemaps and "FARRAY" typemaps is
- * that the "FARRAY" typemaps expect Fortran ordering of
- * multidimensional arrays. In python, the dimensions will not need
- * to be specified (except for the "DATA_TYPE* ARGOUT_ARRAY1"
- * typemaps). The IN_ARRAYs can be a numpy array or any sequence that
- * can be converted to a numpy array of the specified type. The
- * INPLACE_ARRAYs must be numpy arrays of the appropriate type. The
- * ARGOUT_ARRAYs will be returned as new numpy arrays of the
- * appropriate type.
- *
- * These typemaps can be applied to existing functions using the
- * %apply directive. For example:
- *
- * %apply (double* IN_ARRAY1, int DIM1) {(double* series, int length)};
- * double prod(double* series, int length);
- *
- * %apply (int DIM1, int DIM2, double* INPLACE_ARRAY2)
- * {(int rows, int cols, double* matrix )};
- * void floor(int rows, int cols, double* matrix, double f);
- *
- * %apply (double IN_ARRAY3[ANY][ANY][ANY])
- * {(double tensor[2][2][2] )};
- * %apply (double ARGOUT_ARRAY3[ANY][ANY][ANY])
- * {(double low[2][2][2] )};
- * %apply (double ARGOUT_ARRAY3[ANY][ANY][ANY])
- * {(double upp[2][2][2] )};
- * void luSplit(double tensor[2][2][2],
- * double low[2][2][2],
- * double upp[2][2][2] );
- *
- * or directly with
- *
- * double prod(double* IN_ARRAY1, int DIM1);
- *
- * void floor(int DIM1, int DIM2, double* INPLACE_ARRAY2, double f);
- *
- * void luSplit(double IN_ARRAY3[ANY][ANY][ANY],
- * double ARGOUT_ARRAY3[ANY][ANY][ANY],
- * double ARGOUT_ARRAY3[ANY][ANY][ANY]);
- */
-
-%define %numpy_typemaps(DATA_TYPE, DATA_TYPECODE, DIM_TYPE)
-
-/************************/
-/* Input Array Typemaps */
-/************************/
-
-/* Typemap suite for (DATA_TYPE IN_ARRAY1[ANY])
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DATA_TYPE IN_ARRAY1[ANY])
-{
- $1 = is_array($input) || PySequence_Check($input);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DATA_TYPE IN_ARRAY1[ANY])
- (PyArrayObject* array=NULL, int is_new_object=0)
-{
- npy_intp size[1] = { $1_dim0 };
- array = obj_to_array_contiguous_allow_conversion($input,
- DATA_TYPECODE,
- &is_new_object);
- if (!array || !require_dimensions(array, 1) ||
- !require_size(array, size, 1)) SWIG_fail;
- $1 = ($1_ltype) array_data(array);
-}
-%typemap(freearg)
- (DATA_TYPE IN_ARRAY1[ANY])
-{
- if (is_new_object$argnum && array$argnum)
- { Py_DECREF(array$argnum); }
-}
-
-/* Typemap suite for (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1)
-{
- $1 = is_array($input) || PySequence_Check($input);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1)
- (PyArrayObject* array=NULL, int is_new_object=0)
-{
- npy_intp size[1] = { -1 };
- array = obj_to_array_contiguous_allow_conversion($input,
- DATA_TYPECODE,
- &is_new_object);
- if (!array || !require_dimensions(array, 1) ||
- !require_size(array, size, 1)) SWIG_fail;
- $1 = (DATA_TYPE*) array_data(array);
- $2 = (DIM_TYPE) array_size(array,0);
-}
-%typemap(freearg)
- (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1)
-{
- if (is_new_object$argnum && array$argnum)
- { Py_DECREF(array$argnum); }
-}
-
-/* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1)
-{
- $1 = is_array($input) || PySequence_Check($input);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1)
- (PyArrayObject* array=NULL, int is_new_object=0)
-{
- npy_intp size[1] = {-1};
- array = obj_to_array_contiguous_allow_conversion($input,
- DATA_TYPECODE,
- &is_new_object);
- if (!array || !require_dimensions(array, 1) ||
- !require_size(array, size, 1)) SWIG_fail;
- $1 = (DIM_TYPE) array_size(array,0);
- $2 = (DATA_TYPE*) array_data(array);
-}
-%typemap(freearg)
- (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1)
-{
- if (is_new_object$argnum && array$argnum)
- { Py_DECREF(array$argnum); }
-}
-
-/* Typemap suite for (DATA_TYPE IN_ARRAY2[ANY][ANY])
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DATA_TYPE IN_ARRAY2[ANY][ANY])
-{
- $1 = is_array($input) || PySequence_Check($input);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DATA_TYPE IN_ARRAY2[ANY][ANY])
- (PyArrayObject* array=NULL, int is_new_object=0)
-{
- npy_intp size[2] = { $1_dim0, $1_dim1 };
- array = obj_to_array_contiguous_allow_conversion($input,
- DATA_TYPECODE,
- &is_new_object);
- if (!array || !require_dimensions(array, 2) ||
- !require_size(array, size, 2)) SWIG_fail;
- $1 = ($1_ltype) array_data(array);
-}
-%typemap(freearg)
- (DATA_TYPE IN_ARRAY2[ANY][ANY])
-{
- if (is_new_object$argnum && array$argnum)
- { Py_DECREF(array$argnum); }
-}
-
-/* Typemap suite for (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
-{
- $1 = is_array($input) || PySequence_Check($input);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
- (PyArrayObject* array=NULL, int is_new_object=0)
-{
- npy_intp size[2] = { -1, -1 };
- array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE,
- &is_new_object);
- if (!array || !require_dimensions(array, 2) ||
- !require_size(array, size, 2)) SWIG_fail;
- $1 = (DATA_TYPE*) array_data(array);
- $2 = (DIM_TYPE) array_size(array,0);
- $3 = (DIM_TYPE) array_size(array,1);
-}
-%typemap(freearg)
- (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
-{
- if (is_new_object$argnum && array$argnum)
- { Py_DECREF(array$argnum); }
-}
-
-/* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2)
-{
- $1 = is_array($input) || PySequence_Check($input);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2)
- (PyArrayObject* array=NULL, int is_new_object=0)
-{
- npy_intp size[2] = { -1, -1 };
- array = obj_to_array_contiguous_allow_conversion($input,
- DATA_TYPECODE,
- &is_new_object);
- if (!array || !require_dimensions(array, 2) ||
- !require_size(array, size, 2)) SWIG_fail;
- $1 = (DIM_TYPE) array_size(array,0);
- $2 = (DIM_TYPE) array_size(array,1);
- $3 = (DATA_TYPE*) array_data(array);
-}
-%typemap(freearg)
- (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2)
-{
- if (is_new_object$argnum && array$argnum)
- { Py_DECREF(array$argnum); }
-}
-
-/* Typemap suite for (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
-{
- $1 = is_array($input) || PySequence_Check($input);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
- (PyArrayObject* array=NULL, int is_new_object=0)
-{
- npy_intp size[2] = { -1, -1 };
- array = obj_to_array_fortran_allow_conversion($input,
- DATA_TYPECODE,
- &is_new_object);
- if (!array || !require_dimensions(array, 2) ||
- !require_size(array, size, 2) || !require_fortran(array)) SWIG_fail;
- $1 = (DATA_TYPE*) array_data(array);
- $2 = (DIM_TYPE) array_size(array,0);
- $3 = (DIM_TYPE) array_size(array,1);
-}
-%typemap(freearg)
- (DATA_TYPE* IN_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
-{
- if (is_new_object$argnum && array$argnum)
- { Py_DECREF(array$argnum); }
-}
-
-/* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2)
-{
- $1 = is_array($input) || PySequence_Check($input);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2)
- (PyArrayObject* array=NULL, int is_new_object=0)
-{
- npy_intp size[2] = { -1, -1 };
- array = obj_to_array_contiguous_allow_conversion($input,
- DATA_TYPECODE,
- &is_new_object);
- if (!array || !require_dimensions(array, 2) ||
- !require_size(array, size, 2) || !require_fortran(array)) SWIG_fail;
- $1 = (DIM_TYPE) array_size(array,0);
- $2 = (DIM_TYPE) array_size(array,1);
- $3 = (DATA_TYPE*) array_data(array);
-}
-%typemap(freearg)
- (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_FARRAY2)
-{
- if (is_new_object$argnum && array$argnum)
- { Py_DECREF(array$argnum); }
-}
-
-/* Typemap suite for (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY])
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY])
-{
- $1 = is_array($input) || PySequence_Check($input);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY])
- (PyArrayObject* array=NULL, int is_new_object=0)
-{
- npy_intp size[3] = { $1_dim0, $1_dim1, $1_dim2 };
- array = obj_to_array_contiguous_allow_conversion($input,
- DATA_TYPECODE,
- &is_new_object);
- if (!array || !require_dimensions(array, 3) ||
- !require_size(array, size, 3)) SWIG_fail;
- $1 = ($1_ltype) array_data(array);
-}
-%typemap(freearg)
- (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY])
-{
- if (is_new_object$argnum && array$argnum)
- { Py_DECREF(array$argnum); }
-}
-
-/* Typemap suite for (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2,
- * DIM_TYPE DIM3)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
-{
- $1 = is_array($input) || PySequence_Check($input);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
- (PyArrayObject* array=NULL, int is_new_object=0)
-{
- npy_intp size[3] = { -1, -1, -1 };
- array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE,
- &is_new_object);
- if (!array || !require_dimensions(array, 3) ||
- !require_size(array, size, 3)) SWIG_fail;
- $1 = (DATA_TYPE*) array_data(array);
- $2 = (DIM_TYPE) array_size(array,0);
- $3 = (DIM_TYPE) array_size(array,1);
- $4 = (DIM_TYPE) array_size(array,2);
-}
-%typemap(freearg)
- (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
-{
- if (is_new_object$argnum && array$argnum)
- { Py_DECREF(array$argnum); }
-}
-
-/* Typemap suite for (DATA_TYPE** IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2,
- * DIM_TYPE DIM3)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DATA_TYPE** IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
-{
- /* for now, only concerned with lists */
- $1 = PySequence_Check($input);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DATA_TYPE** IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
- (DATA_TYPE** array=NULL, PyArrayObject** object_array=NULL, int* is_new_object_array=NULL)
-{
- npy_intp size[2] = { -1, -1 };
- PyArrayObject* temp_array;
- Py_ssize_t i;
- int is_new_object = 0;
-
- /* length of the list */
- $2 = PyList_Size($input);
-
- /* the arrays */
- array = (DATA_TYPE **)malloc($2*sizeof(DATA_TYPE *));
- object_array = (PyArrayObject **)calloc($2,sizeof(PyArrayObject *));
- is_new_object_array = (int *)calloc($2,sizeof(int));
-
- if (array == NULL || object_array == NULL || is_new_object_array == NULL)
- {
- SWIG_fail;
- }
-
- for (i=0; i<$2; i++)
- {
- temp_array = obj_to_array_contiguous_allow_conversion(PySequence_GetItem($input,i), DATA_TYPECODE, &is_new_object);
-
- /* the new array must be stored so that it can be destroyed in freearg */
- object_array[i] = temp_array;
- is_new_object_array[i] = is_new_object;
-
- if (!temp_array || !require_dimensions(temp_array, 2)) SWIG_fail;
-
- /* store the size of the first array in the list, then use that for comparison. */
- if (i == 0)
- {
- size[0] = array_size(temp_array,0);
- size[1] = array_size(temp_array,1);
- }
-
- if (!require_size(temp_array, size, 2)) SWIG_fail;
-
- array[i] = (DATA_TYPE*) array_data(temp_array);
- }
-
- $1 = (DATA_TYPE**) array;
- $3 = (DIM_TYPE) size[0];
- $4 = (DIM_TYPE) size[1];
-}
-%typemap(freearg)
- (DATA_TYPE** IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
-{
- Py_ssize_t i;
-
- if (array$argnum!=NULL) free(array$argnum);
-
- /*freeing the individual arrays if needed */
- if (object_array$argnum!=NULL)
- {
- if (is_new_object_array$argnum!=NULL)
- {
- for (i=0; i<$2; i++)
- {
- if (object_array$argnum[i] != NULL && is_new_object_array$argnum[i])
- { Py_DECREF(object_array$argnum[i]); }
- }
- free(is_new_object_array$argnum);
- }
- free(object_array$argnum);
- }
-}
-
-/* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3,
- * DATA_TYPE* IN_ARRAY3)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3)
-{
- $1 = is_array($input) || PySequence_Check($input);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3)
- (PyArrayObject* array=NULL, int is_new_object=0)
-{
- npy_intp size[3] = { -1, -1, -1 };
- array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE,
- &is_new_object);
- if (!array || !require_dimensions(array, 3) ||
- !require_size(array, size, 3)) SWIG_fail;
- $1 = (DIM_TYPE) array_size(array,0);
- $2 = (DIM_TYPE) array_size(array,1);
- $3 = (DIM_TYPE) array_size(array,2);
- $4 = (DATA_TYPE*) array_data(array);
-}
-%typemap(freearg)
- (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3)
-{
- if (is_new_object$argnum && array$argnum)
- { Py_DECREF(array$argnum); }
-}
-
-/* Typemap suite for (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2,
- * DIM_TYPE DIM3)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
-{
- $1 = is_array($input) || PySequence_Check($input);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
- (PyArrayObject* array=NULL, int is_new_object=0)
-{
- npy_intp size[3] = { -1, -1, -1 };
- array = obj_to_array_fortran_allow_conversion($input, DATA_TYPECODE,
- &is_new_object);
- if (!array || !require_dimensions(array, 3) ||
- !require_size(array, size, 3) | !require_fortran(array)) SWIG_fail;
- $1 = (DATA_TYPE*) array_data(array);
- $2 = (DIM_TYPE) array_size(array,0);
- $3 = (DIM_TYPE) array_size(array,1);
- $4 = (DIM_TYPE) array_size(array,2);
-}
-%typemap(freearg)
- (DATA_TYPE* IN_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
-{
- if (is_new_object$argnum && array$argnum)
- { Py_DECREF(array$argnum); }
-}
-
-/* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3,
- * DATA_TYPE* IN_FARRAY3)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_FARRAY3)
-{
- $1 = is_array($input) || PySequence_Check($input);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_FARRAY3)
- (PyArrayObject* array=NULL, int is_new_object=0)
-{
- npy_intp size[3] = { -1, -1, -1 };
- array = obj_to_array_contiguous_allow_conversion($input,
- DATA_TYPECODE,
- &is_new_object);
- if (!array || !require_dimensions(array, 3) ||
- !require_size(array, size, 3) || !require_fortran(array)) SWIG_fail;
- $1 = (DIM_TYPE) array_size(array,0);
- $2 = (DIM_TYPE) array_size(array,1);
- $3 = (DIM_TYPE) array_size(array,2);
- $4 = (DATA_TYPE*) array_data(array);
-}
-%typemap(freearg)
- (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_FARRAY3)
-{
- if (is_new_object$argnum && array$argnum)
- { Py_DECREF(array$argnum); }
-}
-
-/* Typemap suite for (DATA_TYPE IN_ARRAY4[ANY][ANY][ANY][ANY])
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DATA_TYPE IN_ARRAY4[ANY][ANY][ANY][ANY])
-{
- $1 = is_array($input) || PySequence_Check($input);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DATA_TYPE IN_ARRAY4[ANY][ANY][ANY][ANY])
- (PyArrayObject* array=NULL, int is_new_object=0)
-{
- npy_intp size[4] = { $1_dim0, $1_dim1, $1_dim2 , $1_dim3};
- array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE,
- &is_new_object);
- if (!array || !require_dimensions(array, 4) ||
- !require_size(array, size, 4)) SWIG_fail;
- $1 = ($1_ltype) array_data(array);
-}
-%typemap(freearg)
- (DATA_TYPE IN_ARRAY4[ANY][ANY][ANY][ANY])
-{
- if (is_new_object$argnum && array$argnum)
- { Py_DECREF(array$argnum); }
-}
-
-/* Typemap suite for (DATA_TYPE* IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2,
- * DIM_TYPE DIM3, DIM_TYPE DIM4)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DATA_TYPE* IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
-{
- $1 = is_array($input) || PySequence_Check($input);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DATA_TYPE* IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
- (PyArrayObject* array=NULL, int is_new_object=0)
-{
- npy_intp size[4] = { -1, -1, -1, -1 };
- array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE,
- &is_new_object);
- if (!array || !require_dimensions(array, 4) ||
- !require_size(array, size, 4)) SWIG_fail;
- $1 = (DATA_TYPE*) array_data(array);
- $2 = (DIM_TYPE) array_size(array,0);
- $3 = (DIM_TYPE) array_size(array,1);
- $4 = (DIM_TYPE) array_size(array,2);
- $5 = (DIM_TYPE) array_size(array,3);
-}
-%typemap(freearg)
- (DATA_TYPE* IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
-{
- if (is_new_object$argnum && array$argnum)
- { Py_DECREF(array$argnum); }
-}
-
-/* Typemap suite for (DATA_TYPE** IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2,
- * DIM_TYPE DIM3, DIM_TYPE DIM4)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DATA_TYPE** IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
-{
- /* for now, only concerned with lists */
- $1 = PySequence_Check($input);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DATA_TYPE** IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
- (DATA_TYPE** array=NULL, PyArrayObject** object_array=NULL, int* is_new_object_array=NULL)
-{
- npy_intp size[3] = { -1, -1, -1 };
- PyArrayObject* temp_array;
- Py_ssize_t i;
- int is_new_object = 0;
-
- /* length of the list */
- $2 = PyList_Size($input);
-
- /* the arrays */
- array = (DATA_TYPE **)malloc($2*sizeof(DATA_TYPE *));
- object_array = (PyArrayObject **)calloc($2,sizeof(PyArrayObject *));
- is_new_object_array = (int *)calloc($2,sizeof(int));
-
- if (array == NULL || object_array == NULL || is_new_object_array == NULL)
- {
- SWIG_fail;
- }
-
- for (i=0; i<$2; i++)
- {
- temp_array = obj_to_array_contiguous_allow_conversion(PySequence_GetItem($input,i), DATA_TYPECODE, &is_new_object);
-
- /* the new array must be stored so that it can be destroyed in freearg */
- object_array[i] = temp_array;
- is_new_object_array[i] = is_new_object;
-
- if (!temp_array || !require_dimensions(temp_array, 3)) SWIG_fail;
-
- /* store the size of the first array in the list, then use that for comparison. */
- if (i == 0)
- {
- size[0] = array_size(temp_array,0);
- size[1] = array_size(temp_array,1);
- size[2] = array_size(temp_array,2);
- }
-
- if (!require_size(temp_array, size, 3)) SWIG_fail;
-
- array[i] = (DATA_TYPE*) array_data(temp_array);
- }
-
- $1 = (DATA_TYPE**) array;
- $3 = (DIM_TYPE) size[0];
- $4 = (DIM_TYPE) size[1];
- $5 = (DIM_TYPE) size[2];
-}
-%typemap(freearg)
- (DATA_TYPE** IN_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
-{
- Py_ssize_t i;
-
- if (array$argnum!=NULL) free(array$argnum);
-
- /*freeing the individual arrays if needed */
- if (object_array$argnum!=NULL)
- {
- if (is_new_object_array$argnum!=NULL)
- {
- for (i=0; i<$2; i++)
- {
- if (object_array$argnum[i] != NULL && is_new_object_array$argnum[i])
- { Py_DECREF(object_array$argnum[i]); }
- }
- free(is_new_object_array$argnum);
- }
- free(object_array$argnum);
- }
-}
-
-/* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4,
- * DATA_TYPE* IN_ARRAY4)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* IN_ARRAY4)
-{
- $1 = is_array($input) || PySequence_Check($input);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* IN_ARRAY4)
- (PyArrayObject* array=NULL, int is_new_object=0)
-{
- npy_intp size[4] = { -1, -1, -1 , -1};
- array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE,
- &is_new_object);
- if (!array || !require_dimensions(array, 4) ||
- !require_size(array, size, 4)) SWIG_fail;
- $1 = (DIM_TYPE) array_size(array,0);
- $2 = (DIM_TYPE) array_size(array,1);
- $3 = (DIM_TYPE) array_size(array,2);
- $4 = (DIM_TYPE) array_size(array,3);
- $5 = (DATA_TYPE*) array_data(array);
-}
-%typemap(freearg)
- (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* IN_ARRAY4)
-{
- if (is_new_object$argnum && array$argnum)
- { Py_DECREF(array$argnum); }
-}
-
-/* Typemap suite for (DATA_TYPE* IN_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2,
- * DIM_TYPE DIM3, DIM_TYPE DIM4)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DATA_TYPE* IN_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
-{
- $1 = is_array($input) || PySequence_Check($input);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DATA_TYPE* IN_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
- (PyArrayObject* array=NULL, int is_new_object=0)
-{
- npy_intp size[4] = { -1, -1, -1, -1 };
- array = obj_to_array_fortran_allow_conversion($input, DATA_TYPECODE,
- &is_new_object);
- if (!array || !require_dimensions(array, 4) ||
- !require_size(array, size, 4) | !require_fortran(array)) SWIG_fail;
- $1 = (DATA_TYPE*) array_data(array);
- $2 = (DIM_TYPE) array_size(array,0);
- $3 = (DIM_TYPE) array_size(array,1);
- $4 = (DIM_TYPE) array_size(array,2);
- $5 = (DIM_TYPE) array_size(array,3);
-}
-%typemap(freearg)
- (DATA_TYPE* IN_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
-{
- if (is_new_object$argnum && array$argnum)
- { Py_DECREF(array$argnum); }
-}
-
-/* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4,
- * DATA_TYPE* IN_FARRAY4)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* IN_FARRAY4)
-{
- $1 = is_array($input) || PySequence_Check($input);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* IN_FARRAY4)
- (PyArrayObject* array=NULL, int is_new_object=0)
-{
- npy_intp size[4] = { -1, -1, -1 , -1 };
- array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE,
- &is_new_object);
- if (!array || !require_dimensions(array, 4) ||
- !require_size(array, size, 4) || !require_fortran(array)) SWIG_fail;
- $1 = (DIM_TYPE) array_size(array,0);
- $2 = (DIM_TYPE) array_size(array,1);
- $3 = (DIM_TYPE) array_size(array,2);
- $4 = (DIM_TYPE) array_size(array,3);
- $5 = (DATA_TYPE*) array_data(array);
-}
-%typemap(freearg)
- (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* IN_FARRAY4)
-{
- if (is_new_object$argnum && array$argnum)
- { Py_DECREF(array$argnum); }
-}
-
-/***************************/
-/* In-Place Array Typemaps */
-/***************************/
-
-/* Typemap suite for (DATA_TYPE INPLACE_ARRAY1[ANY])
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DATA_TYPE INPLACE_ARRAY1[ANY])
-{
- $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
- DATA_TYPECODE);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DATA_TYPE INPLACE_ARRAY1[ANY])
- (PyArrayObject* array=NULL)
-{
- npy_intp size[1] = { $1_dim0 };
- array = obj_to_array_no_conversion($input, DATA_TYPECODE);
- if (!array || !require_dimensions(array,1) || !require_size(array, size, 1) ||
- !require_contiguous(array) || !require_native(array)) SWIG_fail;
- $1 = ($1_ltype) array_data(array);
-}
-
-/* Typemap suite for (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1)
-{
- $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
- DATA_TYPECODE);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1)
- (PyArrayObject* array=NULL, int i=1)
-{
- array = obj_to_array_no_conversion($input, DATA_TYPECODE);
- if (!array || !require_dimensions(array,1) || !require_contiguous(array)
- || !require_native(array)) SWIG_fail;
- $1 = (DATA_TYPE*) array_data(array);
- $2 = 1;
- for (i=0; i < array_numdims(array); ++i) $2 *= array_size(array,i);
-}
-
-/* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1)
-{
- $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
- DATA_TYPECODE);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1)
- (PyArrayObject* array=NULL, int i=0)
-{
- array = obj_to_array_no_conversion($input, DATA_TYPECODE);
- if (!array || !require_dimensions(array,1) || !require_contiguous(array)
- || !require_native(array)) SWIG_fail;
- $1 = 1;
- for (i=0; i < array_numdims(array); ++i) $1 *= array_size(array,i);
- $2 = (DATA_TYPE*) array_data(array);
-}
-
-/* Typemap suite for (DATA_TYPE INPLACE_ARRAY2[ANY][ANY])
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DATA_TYPE INPLACE_ARRAY2[ANY][ANY])
-{
- $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
- DATA_TYPECODE);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DATA_TYPE INPLACE_ARRAY2[ANY][ANY])
- (PyArrayObject* array=NULL)
-{
- npy_intp size[2] = { $1_dim0, $1_dim1 };
- array = obj_to_array_no_conversion($input, DATA_TYPECODE);
- if (!array || !require_dimensions(array,2) || !require_size(array, size, 2) ||
- !require_contiguous(array) || !require_native(array)) SWIG_fail;
- $1 = ($1_ltype) array_data(array);
-}
-
-/* Typemap suite for (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
-{
- $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
- DATA_TYPECODE);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
- (PyArrayObject* array=NULL)
-{
- array = obj_to_array_no_conversion($input, DATA_TYPECODE);
- if (!array || !require_dimensions(array,2) || !require_contiguous(array)
- || !require_native(array)) SWIG_fail;
- $1 = (DATA_TYPE*) array_data(array);
- $2 = (DIM_TYPE) array_size(array,0);
- $3 = (DIM_TYPE) array_size(array,1);
-}
-
-/* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2)
-{
- $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
- DATA_TYPECODE);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2)
- (PyArrayObject* array=NULL)
-{
- array = obj_to_array_no_conversion($input, DATA_TYPECODE);
- if (!array || !require_dimensions(array,2) || !require_contiguous(array) ||
- !require_native(array)) SWIG_fail;
- $1 = (DIM_TYPE) array_size(array,0);
- $2 = (DIM_TYPE) array_size(array,1);
- $3 = (DATA_TYPE*) array_data(array);
-}
-
-/* Typemap suite for (DATA_TYPE* INPLACE_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DATA_TYPE* INPLACE_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
-{
- $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
- DATA_TYPECODE);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DATA_TYPE* INPLACE_FARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
- (PyArrayObject* array=NULL)
-{
- array = obj_to_array_no_conversion($input, DATA_TYPECODE);
- if (!array || !require_dimensions(array,2) || !require_contiguous(array)
- || !require_native(array) || !require_fortran(array)) SWIG_fail;
- $1 = (DATA_TYPE*) array_data(array);
- $2 = (DIM_TYPE) array_size(array,0);
- $3 = (DIM_TYPE) array_size(array,1);
-}
-
-/* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_FARRAY2)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_FARRAY2)
-{
- $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
- DATA_TYPECODE);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_FARRAY2)
- (PyArrayObject* array=NULL)
-{
- array = obj_to_array_no_conversion($input, DATA_TYPECODE);
- if (!array || !require_dimensions(array,2) || !require_contiguous(array) ||
- !require_native(array) || !require_fortran(array)) SWIG_fail;
- $1 = (DIM_TYPE) array_size(array,0);
- $2 = (DIM_TYPE) array_size(array,1);
- $3 = (DATA_TYPE*) array_data(array);
-}
-
-/* Typemap suite for (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY])
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY])
-{
- $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
- DATA_TYPECODE);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY])
- (PyArrayObject* array=NULL)
-{
- npy_intp size[3] = { $1_dim0, $1_dim1, $1_dim2 };
- array = obj_to_array_no_conversion($input, DATA_TYPECODE);
- if (!array || !require_dimensions(array,3) || !require_size(array, size, 3) ||
- !require_contiguous(array) || !require_native(array)) SWIG_fail;
- $1 = ($1_ltype) array_data(array);
-}
-
-/* Typemap suite for (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2,
- * DIM_TYPE DIM3)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
-{
- $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
- DATA_TYPECODE);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
- (PyArrayObject* array=NULL)
-{
- array = obj_to_array_no_conversion($input, DATA_TYPECODE);
- if (!array || !require_dimensions(array,3) || !require_contiguous(array) ||
- !require_native(array)) SWIG_fail;
- $1 = (DATA_TYPE*) array_data(array);
- $2 = (DIM_TYPE) array_size(array,0);
- $3 = (DIM_TYPE) array_size(array,1);
- $4 = (DIM_TYPE) array_size(array,2);
-}
-
-/* Typemap suite for (DATA_TYPE** INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2,
- * DIM_TYPE DIM3)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DATA_TYPE** INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
-{
- $1 = PySequence_Check($input);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DATA_TYPE** INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
- (DATA_TYPE** array=NULL, PyArrayObject** object_array=NULL)
-{
- npy_intp size[2] = { -1, -1 };
- PyArrayObject* temp_array;
- Py_ssize_t i;
-
- /* length of the list */
- $2 = PyList_Size($input);
-
- /* the arrays */
- array = (DATA_TYPE **)malloc($2*sizeof(DATA_TYPE *));
- object_array = (PyArrayObject **)calloc($2,sizeof(PyArrayObject *));
-
- if (array == NULL || object_array == NULL)
- {
- SWIG_fail;
- }
-
- for (i=0; i<$2; i++)
- {
- temp_array = obj_to_array_no_conversion(PySequence_GetItem($input,i), DATA_TYPECODE);
-
- /* the new array must be stored so that it can be destroyed in freearg */
- object_array[i] = temp_array;
-
- if ( !temp_array || !require_dimensions(temp_array, 2) ||
- !require_contiguous(temp_array) ||
- !require_native(temp_array) ||
- !PyArray_EquivTypenums(array_type(temp_array), DATA_TYPECODE)
- ) SWIG_fail;
-
- /* store the size of the first array in the list, then use that for comparison. */
- if (i == 0)
- {
- size[0] = array_size(temp_array,0);
- size[1] = array_size(temp_array,1);
- }
-
- if (!require_size(temp_array, size, 2)) SWIG_fail;
-
- array[i] = (DATA_TYPE*) array_data(temp_array);
- }
-
- $1 = (DATA_TYPE**) array;
- $3 = (DIM_TYPE) size[0];
- $4 = (DIM_TYPE) size[1];
-}
-%typemap(freearg)
- (DATA_TYPE** INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
-{
- if (array$argnum!=NULL) free(array$argnum);
- if (object_array$argnum!=NULL) free(object_array$argnum);
-}
-
-/* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3,
- * DATA_TYPE* INPLACE_ARRAY3)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_ARRAY3)
-{
- $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
- DATA_TYPECODE);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_ARRAY3)
- (PyArrayObject* array=NULL)
-{
- array = obj_to_array_no_conversion($input, DATA_TYPECODE);
- if (!array || !require_dimensions(array,3) || !require_contiguous(array)
- || !require_native(array)) SWIG_fail;
- $1 = (DIM_TYPE) array_size(array,0);
- $2 = (DIM_TYPE) array_size(array,1);
- $3 = (DIM_TYPE) array_size(array,2);
- $4 = (DATA_TYPE*) array_data(array);
-}
-
-/* Typemap suite for (DATA_TYPE* INPLACE_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2,
- * DIM_TYPE DIM3)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DATA_TYPE* INPLACE_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
-{
- $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
- DATA_TYPECODE);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DATA_TYPE* INPLACE_FARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
- (PyArrayObject* array=NULL)
-{
- array = obj_to_array_no_conversion($input, DATA_TYPECODE);
- if (!array || !require_dimensions(array,3) || !require_contiguous(array) ||
- !require_native(array) || !require_fortran(array)) SWIG_fail;
- $1 = (DATA_TYPE*) array_data(array);
- $2 = (DIM_TYPE) array_size(array,0);
- $3 = (DIM_TYPE) array_size(array,1);
- $4 = (DIM_TYPE) array_size(array,2);
-}
-
-/* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3,
- * DATA_TYPE* INPLACE_FARRAY3)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_FARRAY3)
-{
- $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
- DATA_TYPECODE);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_FARRAY3)
- (PyArrayObject* array=NULL)
-{
- array = obj_to_array_no_conversion($input, DATA_TYPECODE);
- if (!array || !require_dimensions(array,3) || !require_contiguous(array)
- || !require_native(array) || !require_fortran(array)) SWIG_fail;
- $1 = (DIM_TYPE) array_size(array,0);
- $2 = (DIM_TYPE) array_size(array,1);
- $3 = (DIM_TYPE) array_size(array,2);
- $4 = (DATA_TYPE*) array_data(array);
-}
-
-/* Typemap suite for (DATA_TYPE INPLACE_ARRAY4[ANY][ANY][ANY][ANY])
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DATA_TYPE INPLACE_ARRAY4[ANY][ANY][ANY][ANY])
-{
- $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
- DATA_TYPECODE);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DATA_TYPE INPLACE_ARRAY4[ANY][ANY][ANY][ANY])
- (PyArrayObject* array=NULL)
-{
- npy_intp size[4] = { $1_dim0, $1_dim1, $1_dim2 , $1_dim3 };
- array = obj_to_array_no_conversion($input, DATA_TYPECODE);
- if (!array || !require_dimensions(array,4) || !require_size(array, size, 4) ||
- !require_contiguous(array) || !require_native(array)) SWIG_fail;
- $1 = ($1_ltype) array_data(array);
-}
-
-/* Typemap suite for (DATA_TYPE* INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2,
- * DIM_TYPE DIM3, DIM_TYPE DIM4)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DATA_TYPE* INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
-{
- $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
- DATA_TYPECODE);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DATA_TYPE* INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
- (PyArrayObject* array=NULL)
-{
- array = obj_to_array_no_conversion($input, DATA_TYPECODE);
- if (!array || !require_dimensions(array,4) || !require_contiguous(array) ||
- !require_native(array)) SWIG_fail;
- $1 = (DATA_TYPE*) array_data(array);
- $2 = (DIM_TYPE) array_size(array,0);
- $3 = (DIM_TYPE) array_size(array,1);
- $4 = (DIM_TYPE) array_size(array,2);
- $5 = (DIM_TYPE) array_size(array,3);
-}
-
-/* Typemap suite for (DATA_TYPE** INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2,
- * DIM_TYPE DIM3, DIM_TYPE DIM4)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DATA_TYPE** INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
-{
- $1 = PySequence_Check($input);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DATA_TYPE** INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
- (DATA_TYPE** array=NULL, PyArrayObject** object_array=NULL)
-{
- npy_intp size[3] = { -1, -1, -1 };
- PyArrayObject* temp_array;
- Py_ssize_t i;
-
- /* length of the list */
- $2 = PyList_Size($input);
-
- /* the arrays */
- array = (DATA_TYPE **)malloc($2*sizeof(DATA_TYPE *));
- object_array = (PyArrayObject **)calloc($2,sizeof(PyArrayObject *));
-
- if (array == NULL || object_array == NULL)
- {
- SWIG_fail;
- }
-
- for (i=0; i<$2; i++)
- {
- temp_array = obj_to_array_no_conversion(PySequence_GetItem($input,i), DATA_TYPECODE);
-
- /* the new array must be stored so that it can be destroyed in freearg */
- object_array[i] = temp_array;
-
- if ( !temp_array || !require_dimensions(temp_array, 3) ||
- !require_contiguous(temp_array) ||
- !require_native(temp_array) ||
- !PyArray_EquivTypenums(array_type(temp_array), DATA_TYPECODE)
- ) SWIG_fail;
-
- /* store the size of the first array in the list, then use that for comparison. */
- if (i == 0)
- {
- size[0] = array_size(temp_array,0);
- size[1] = array_size(temp_array,1);
- size[2] = array_size(temp_array,2);
- }
-
- if (!require_size(temp_array, size, 3)) SWIG_fail;
-
- array[i] = (DATA_TYPE*) array_data(temp_array);
- }
-
- $1 = (DATA_TYPE**) array;
- $3 = (DIM_TYPE) size[0];
- $4 = (DIM_TYPE) size[1];
- $5 = (DIM_TYPE) size[2];
-}
-%typemap(freearg)
- (DATA_TYPE** INPLACE_ARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
-{
- if (array$argnum!=NULL) free(array$argnum);
- if (object_array$argnum!=NULL) free(object_array$argnum);
-}
-
-/* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4,
- * DATA_TYPE* INPLACE_ARRAY4)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* INPLACE_ARRAY4)
-{
- $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
- DATA_TYPECODE);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* INPLACE_ARRAY4)
- (PyArrayObject* array=NULL)
-{
- array = obj_to_array_no_conversion($input, DATA_TYPECODE);
- if (!array || !require_dimensions(array,4) || !require_contiguous(array)
- || !require_native(array)) SWIG_fail;
- $1 = (DIM_TYPE) array_size(array,0);
- $2 = (DIM_TYPE) array_size(array,1);
- $3 = (DIM_TYPE) array_size(array,2);
- $4 = (DIM_TYPE) array_size(array,3);
- $5 = (DATA_TYPE*) array_data(array);
-}
-
-/* Typemap suite for (DATA_TYPE* INPLACE_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2,
- * DIM_TYPE DIM3, DIM_TYPE DIM4)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DATA_TYPE* INPLACE_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
-{
- $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
- DATA_TYPECODE);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DATA_TYPE* INPLACE_FARRAY4, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4)
- (PyArrayObject* array=NULL)
-{
- array = obj_to_array_no_conversion($input, DATA_TYPECODE);
- if (!array || !require_dimensions(array,4) || !require_contiguous(array) ||
- !require_native(array) || !require_fortran(array)) SWIG_fail;
- $1 = (DATA_TYPE*) array_data(array);
- $2 = (DIM_TYPE) array_size(array,0);
- $3 = (DIM_TYPE) array_size(array,1);
- $4 = (DIM_TYPE) array_size(array,2);
- $5 = (DIM_TYPE) array_size(array,3);
-}
-
-/* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3,
- * DATA_TYPE* INPLACE_FARRAY4)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
- fragment="NumPy_Macros")
- (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* INPLACE_FARRAY4)
-{
- $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
- DATA_TYPECODE);
-}
-%typemap(in,
- fragment="NumPy_Fragments")
- (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DIM_TYPE DIM4, DATA_TYPE* INPLACE_FARRAY4)
- (PyArrayObject* array=NULL)
-{
- array = obj_to_array_no_conversion($input, DATA_TYPECODE);
- if (!array || !require_dimensions(array,4) || !require_contiguous(array)
- || !require_native(array) || !require_fortran(array)) SWIG_fail;
- $1 = (DIM_TYPE) array_size(array,0);
- $2 = (DIM_TYPE) array_size(array,1);
- $3 = (DIM_TYPE) array_size(array,2);
- $4 = (DIM_TYPE) array_size(array,3);
- $5 = (DATA_TYPE*) array_data(array);
-}
-
-/*************************/
-/* Argout Array Typemaps */
-/*************************/
-
-/* Typemap suite for (DATA_TYPE ARGOUT_ARRAY1[ANY])
- */
-%typemap(in,numinputs=0,
- fragment="NumPy_Backward_Compatibility,NumPy_Macros")
- (DATA_TYPE ARGOUT_ARRAY1[ANY])
- (PyObject* array = NULL)
-{
- npy_intp dims[1] = { $1_dim0 };
- array = PyArray_SimpleNew(1, dims, DATA_TYPECODE);
- if (!array) SWIG_fail;
- $1 = ($1_ltype) array_data(array);
-}
-%typemap(argout)
- (DATA_TYPE ARGOUT_ARRAY1[ANY])
-{
- $result = SWIG_Python_AppendOutput($result,(PyObject*)array$argnum);
-}
-
-/* Typemap suite for (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1)
- */
-%typemap(in,numinputs=1,
- fragment="NumPy_Fragments")
- (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1)
- (PyObject* array = NULL)
-{
- npy_intp dims[1];
- if (!PyInt_Check($input))
- {
- const char* typestring = pytype_string($input);
- PyErr_Format(PyExc_TypeError,
- "Int dimension expected. '%s' given.",
- typestring);
- SWIG_fail;
- }
- $2 = (DIM_TYPE) PyInt_AsLong($input);
- dims[0] = (npy_intp) $2;
- array = PyArray_SimpleNew(1, dims, DATA_TYPECODE);
- if (!array) SWIG_fail;
- $1 = (DATA_TYPE*) array_data(array);
-}
-%typemap(argout)
- (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1)
-{
- $result = SWIG_Python_AppendOutput($result,(PyObject*)array$argnum);
-}
-
-/* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1)
- */
-%typemap(in,numinputs=1,
- fragment="NumPy_Fragments")
- (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1)
- (PyObject* array = NULL)
-{
- npy_intp dims[1];
- if (!PyInt_Check($input))
- {
- const char* typestring = pytype_string($input);
- PyErr_Format(PyExc_TypeError,
- "Int dimension expected. '%s' given.",
- typestring);
- SWIG_fail;
- }
- $1 = (DIM_TYPE) PyInt_AsLong($input);
- dims[0] = (npy_intp) $1;
- array = PyArray_SimpleNew(1, dims, DATA_TYPECODE);
- if (!array) SWIG_fail;
- $2 = (DATA_TYPE*) array_data(array);
-}
-%typemap(argout)
- (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1)
-{
- $result = SWIG_Python_AppendOutput($result,(PyObject*)array$argnum);
-}
-
-/* Typemap suite for (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY])
- */
-%typemap(in,numinputs=0,
- fragment="NumPy_Backward_Compatibility,NumPy_Macros")
- (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY])
- (PyObject* array = NULL)
-{
- npy_intp dims[2] = { $1_dim0, $1_dim1 };
- array = PyArray_SimpleNew(2, dims, DATA_TYPECODE);
- if (!array) SWIG_fail;
- $1 = ($1_ltype) array_data(array);
-}
-%typemap(argout)
- (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY])
-{
- $result = SWIG_Python_AppendOutput($result,(PyObject*)array$argnum);
-}
-
-/* Typemap suite for (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY])
- */
-%typemap(in,numinputs=0,
- fragment="NumPy_Backward_Compatibility,NumPy_Macros")
- (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY])
- (PyObject* array = NULL)
-{
- npy_intp dims[3] = { $1_dim0, $1_dim1, $1_dim2 };
- array = PyArray_SimpleNew(3, dims, DATA_TYPECODE);
- if (!array) SWIG_fail;
- $1 = ($1_ltype) array_data(array);
-}
-%typemap(argout)
- (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY])
-{
- $result = SWIG_Python_AppendOutput($result,(PyObject*)array$argnum);
-}
-
-/* Typemap suite for (DATA_TYPE ARGOUT_ARRAY4[ANY][ANY][ANY][ANY])
- */
-%typemap(in,numinputs=0,
- fragment="NumPy_Backward_Compatibility,NumPy_Macros")
- (DATA_TYPE ARGOUT_ARRAY4[ANY][ANY][ANY][ANY])
- (PyObject* array = NULL)
-{
- npy_intp dims[4] = { $1_dim0, $1_dim1, $1_dim2, $1_dim3 };
- array = PyArray_SimpleNew(4, dims, DATA_TYPECODE);
- if (!array) SWIG_fail;
- $1 = ($1_ltype) array_data(array);
-}
-%typemap(argout)
- (DATA_TYPE ARGOUT_ARRAY4[ANY][ANY][ANY][ANY])
-{
- $result = SWIG_Python_AppendOutput($result,(PyObject*)array$argnum);
-}
-
-/*****************************/
-/* Argoutview Array Typemaps */
-/*****************************/
-
-/* Typemap suite for (DATA_TYPE** ARGOUTVIEW_ARRAY1, DIM_TYPE* DIM1)
- */
-%typemap(in,numinputs=0)
- (DATA_TYPE** ARGOUTVIEW_ARRAY1, DIM_TYPE* DIM1 )
- (DATA_TYPE* data_temp = NULL , DIM_TYPE dim_temp)
-{
- $1 = &data_temp;
- $2 = &dim_temp;
-}
-%typemap(argout,
- fragment="NumPy_Backward_Compatibility")
- (DATA_TYPE** ARGOUTVIEW_ARRAY1, DIM_TYPE* DIM1)
-{
- npy_intp dims[1] = { *$2 };
- PyObject* obj = PyArray_SimpleNewFromData(1, dims, DATA_TYPECODE, (void*)(*$1));
- PyArrayObject* array = (PyArrayObject*) obj;
-
- if (!array) SWIG_fail;
- $result = SWIG_Python_AppendOutput($result,obj);
-}
-
-/* Typemap suite for (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEW_ARRAY1)
- */
-%typemap(in,numinputs=0)
- (DIM_TYPE* DIM1 , DATA_TYPE** ARGOUTVIEW_ARRAY1)
- (DIM_TYPE dim_temp, DATA_TYPE* data_temp = NULL )
-{
- $1 = &dim_temp;
- $2 = &data_temp;
-}
-%typemap(argout,
- fragment="NumPy_Backward_Compatibility")
- (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEW_ARRAY1)
-{
- npy_intp dims[1] = { *$1 };
- PyObject* obj = PyArray_SimpleNewFromData(1, dims, DATA_TYPECODE, (void*)(*$2));
- PyArrayObject* array = (PyArrayObject*) obj;
-
- if (!array) SWIG_fail;
- $result = SWIG_Python_AppendOutput($result,obj);
-}
-
-/* Typemap suite for (DATA_TYPE** ARGOUTVIEW_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2)
- */
-%typemap(in,numinputs=0)
- (DATA_TYPE** ARGOUTVIEW_ARRAY2, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 )
- (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp)
-{
- $1 = &data_temp;
- $2 = &dim1_temp;
- $3 = &dim2_temp;
-}
-%typemap(argout,
- fragment="NumPy_Backward_Compatibility")
- (DATA_TYPE** ARGOUTVIEW_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2)
-{
- npy_intp dims[2] = { *$2, *$3 };
- PyObject* obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$1));
- PyArrayObject* array = (PyArrayObject*) obj;
-
- if (!array) SWIG_fail;
- $result = SWIG_Python_AppendOutput($result,obj);
-}
-
-/* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_ARRAY2)
- */
-%typemap(in,numinputs=0)
- (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DATA_TYPE** ARGOUTVIEW_ARRAY2)
- (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DATA_TYPE* data_temp = NULL )
-{
- $1 = &dim1_temp;
- $2 = &dim2_temp;
- $3 = &data_temp;
-}
-%typemap(argout,
- fragment="NumPy_Backward_Compatibility")
- (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_ARRAY2)
-{
- npy_intp dims[2] = { *$1, *$2 };
- PyObject* obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$3));
- PyArrayObject* array = (PyArrayObject*) obj;
-
- if (!array) SWIG_fail;
- $result = SWIG_Python_AppendOutput($result,obj);
-}
-
-/* Typemap suite for (DATA_TYPE** ARGOUTVIEW_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2)
- */
-%typemap(in,numinputs=0)
- (DATA_TYPE** ARGOUTVIEW_FARRAY2, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 )
- (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp)
-{
- $1 = &data_temp;
- $2 = &dim1_temp;
- $3 = &dim2_temp;
-}
-%typemap(argout,
- fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements")
- (DATA_TYPE** ARGOUTVIEW_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2)
-{
- npy_intp dims[2] = { *$2, *$3 };
- PyObject* obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$1));
- PyArrayObject* array = (PyArrayObject*) obj;
-
- if (!array || !require_fortran(array)) SWIG_fail;
- $result = SWIG_Python_AppendOutput($result,obj);
-}
-
-/* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_FARRAY2)
- */
-%typemap(in,numinputs=0)
- (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DATA_TYPE** ARGOUTVIEW_FARRAY2)
- (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DATA_TYPE* data_temp = NULL )
-{
- $1 = &dim1_temp;
- $2 = &dim2_temp;
- $3 = &data_temp;
-}
-%typemap(argout,
- fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements")
- (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEW_FARRAY2)
-{
- npy_intp dims[2] = { *$1, *$2 };
- PyObject* obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$3));
- PyArrayObject* array = (PyArrayObject*) obj;
-
- if (!array || !require_fortran(array)) SWIG_fail;
- $result = SWIG_Python_AppendOutput($result,obj);
-}
-
-/* Typemap suite for (DATA_TYPE** ARGOUTVIEW_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2,
- DIM_TYPE* DIM3)
- */
-%typemap(in,numinputs=0)
- (DATA_TYPE** ARGOUTVIEW_ARRAY3, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 )
- (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp)
-{
- $1 = &data_temp;
- $2 = &dim1_temp;
- $3 = &dim2_temp;
- $4 = &dim3_temp;
-}
-%typemap(argout,
- fragment="NumPy_Backward_Compatibility")
- (DATA_TYPE** ARGOUTVIEW_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3)
-{
- npy_intp dims[3] = { *$2, *$3, *$4 };
- PyObject* obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$1));
- PyArrayObject* array = (PyArrayObject*) obj;
-
- if (!array) SWIG_fail;
- $result = SWIG_Python_AppendOutput($result,obj);
-}
-
-/* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3,
- DATA_TYPE** ARGOUTVIEW_ARRAY3)
- */
-%typemap(in,numinputs=0)
- (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_ARRAY3)
- (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DATA_TYPE* data_temp = NULL)
-{
- $1 = &dim1_temp;
- $2 = &dim2_temp;
- $3 = &dim3_temp;
- $4 = &data_temp;
-}
-%typemap(argout,
- fragment="NumPy_Backward_Compatibility")
- (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_ARRAY3)
-{
- npy_intp dims[3] = { *$1, *$2, *$3 };
- PyObject* obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$4));
- PyArrayObject* array = (PyArrayObject*) obj;
-
- if (!array) SWIG_fail;
- $result = SWIG_Python_AppendOutput($result,obj);
-}
-
-/* Typemap suite for (DATA_TYPE** ARGOUTVIEW_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2,
- DIM_TYPE* DIM3)
- */
-%typemap(in,numinputs=0)
- (DATA_TYPE** ARGOUTVIEW_FARRAY3, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 )
- (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp)
-{
- $1 = &data_temp;
- $2 = &dim1_temp;
- $3 = &dim2_temp;
- $4 = &dim3_temp;
-}
-%typemap(argout,
- fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements")
- (DATA_TYPE** ARGOUTVIEW_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3)
-{
- npy_intp dims[3] = { *$2, *$3, *$4 };
- PyObject* obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$1));
- PyArrayObject* array = (PyArrayObject*) obj;
-
- if (!array || !require_fortran(array)) SWIG_fail;
- $result = SWIG_Python_AppendOutput($result,obj);
-}
-
-/* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3,
- DATA_TYPE** ARGOUTVIEW_FARRAY3)
- */
-%typemap(in,numinputs=0)
- (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DATA_TYPE** ARGOUTVIEW_FARRAY3)
- (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DATA_TYPE* data_temp = NULL )
-{
- $1 = &dim1_temp;
- $2 = &dim2_temp;
- $3 = &dim3_temp;
- $4 = &data_temp;
-}
-%typemap(argout,
- fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements")
- (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEW_FARRAY3)
-{
- npy_intp dims[3] = { *$1, *$2, *$3 };
- PyObject* obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$4));
- PyArrayObject* array = (PyArrayObject*) obj;
-
- if (!array || !require_fortran(array)) SWIG_fail;
- $result = SWIG_Python_AppendOutput($result,obj);
-}
-
-/* Typemap suite for (DATA_TYPE** ARGOUTVIEW_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2,
- DIM_TYPE* DIM3, DIM_TYPE* DIM4)
- */
-%typemap(in,numinputs=0)
- (DATA_TYPE** ARGOUTVIEW_ARRAY4, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 )
- (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp)
-{
- $1 = &data_temp;
- $2 = &dim1_temp;
- $3 = &dim2_temp;
- $4 = &dim3_temp;
- $5 = &dim4_temp;
-}
-%typemap(argout,
- fragment="NumPy_Backward_Compatibility")
- (DATA_TYPE** ARGOUTVIEW_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4)
-{
- npy_intp dims[4] = { *$2, *$3, *$4 , *$5 };
- PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$1));
- PyArrayObject* array = (PyArrayObject*) obj;
-
- if (!array) SWIG_fail;
- $result = SWIG_Python_AppendOutput($result,obj);
-}
-
-/* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4,
- DATA_TYPE** ARGOUTVIEW_ARRAY4)
- */
-%typemap(in,numinputs=0)
- (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 , DATA_TYPE** ARGOUTVIEW_ARRAY4)
- (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp, DATA_TYPE* data_temp = NULL )
-{
- $1 = &dim1_temp;
- $2 = &dim2_temp;
- $3 = &dim3_temp;
- $4 = &dim4_temp;
- $5 = &data_temp;
-}
-%typemap(argout,
- fragment="NumPy_Backward_Compatibility")
- (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEW_ARRAY4)
-{
- npy_intp dims[4] = { *$1, *$2, *$3 , *$4 };
- PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$5));
- PyArrayObject* array = (PyArrayObject*) obj;
-
- if (!array) SWIG_fail;
- $result = SWIG_Python_AppendOutput($result,obj);
-}
-
-/* Typemap suite for (DATA_TYPE** ARGOUTVIEW_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2,
- DIM_TYPE* DIM3, DIM_TYPE* DIM4)
- */
-%typemap(in,numinputs=0)
- (DATA_TYPE** ARGOUTVIEW_FARRAY4, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 )
- (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp)
-{
- $1 = &data_temp;
- $2 = &dim1_temp;
- $3 = &dim2_temp;
- $4 = &dim3_temp;
- $5 = &dim4_temp;
-}
-%typemap(argout,
- fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements")
- (DATA_TYPE** ARGOUTVIEW_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4)
-{
- npy_intp dims[4] = { *$2, *$3, *$4 , *$5 };
- PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$1));
- PyArrayObject* array = (PyArrayObject*) obj;
-
- if (!array || !require_fortran(array)) SWIG_fail;
- $result = SWIG_Python_AppendOutput($result,obj);
-}
-
-/* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4,
- DATA_TYPE** ARGOUTVIEW_FARRAY4)
- */
-%typemap(in,numinputs=0)
- (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 , DATA_TYPE** ARGOUTVIEW_FARRAY4)
- (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp, DATA_TYPE* data_temp = NULL )
-{
- $1 = &dim1_temp;
- $2 = &dim2_temp;
- $3 = &dim3_temp;
- $4 = &dim4_temp;
- $5 = &data_temp;
-}
-%typemap(argout,
- fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements")
- (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEW_FARRAY4)
-{
- npy_intp dims[4] = { *$1, *$2, *$3 , *$4 };
- PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$5));
- PyArrayObject* array = (PyArrayObject*) obj;
-
- if (!array || !require_fortran(array)) SWIG_fail;
- $result = SWIG_Python_AppendOutput($result,obj);
-}
-
-/*************************************/
-/* Managed Argoutview Array Typemaps */
-/*************************************/
-
-/* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_ARRAY1, DIM_TYPE* DIM1)
- */
-%typemap(in,numinputs=0)
- (DATA_TYPE** ARGOUTVIEWM_ARRAY1, DIM_TYPE* DIM1 )
- (DATA_TYPE* data_temp = NULL , DIM_TYPE dim_temp)
-{
- $1 = &data_temp;
- $2 = &dim_temp;
-}
-%typemap(argout,
- fragment="NumPy_Backward_Compatibility")
- (DATA_TYPE** ARGOUTVIEWM_ARRAY1, DIM_TYPE* DIM1)
-{
- npy_intp dims[1] = { *$2 };
- PyObject* obj = PyArray_SimpleNewFromData(1, dims, DATA_TYPECODE, (void*)(*$1));
- PyArrayObject* array = (PyArrayObject*) obj;
-
- if (!array) SWIG_fail;
-
-%#ifdef SWIGPY_USE_CAPSULE
- PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
-%#else
- PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
-%#endif
-
-%#if NPY_API_VERSION < 0x00000007
- PyArray_BASE(array) = cap;
-%#else
- PyArray_SetBaseObject(array,cap);
-%#endif
-
- $result = SWIG_Python_AppendOutput($result,obj);
-}
-
-/* Typemap suite for (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEWM_ARRAY1)
- */
-%typemap(in,numinputs=0)
- (DIM_TYPE* DIM1 , DATA_TYPE** ARGOUTVIEWM_ARRAY1)
- (DIM_TYPE dim_temp, DATA_TYPE* data_temp = NULL )
-{
- $1 = &dim_temp;
- $2 = &data_temp;
-}
-%typemap(argout,
- fragment="NumPy_Backward_Compatibility")
- (DIM_TYPE* DIM1, DATA_TYPE** ARGOUTVIEWM_ARRAY1)
-{
- npy_intp dims[1] = { *$1 };
- PyObject* obj = PyArray_SimpleNewFromData(1, dims, DATA_TYPECODE, (void*)(*$2));
- PyArrayObject* array = (PyArrayObject*) obj;
-
- if (!array) SWIG_fail;
-
-%#ifdef SWIGPY_USE_CAPSULE
- PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
-%#else
- PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
-%#endif
-
-%#if NPY_API_VERSION < 0x00000007
- PyArray_BASE(array) = cap;
-%#else
- PyArray_SetBaseObject(array,cap);
-%#endif
-
- $result = SWIG_Python_AppendOutput($result,obj);
-}
-
-/* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2)
- */
-%typemap(in,numinputs=0)
- (DATA_TYPE** ARGOUTVIEWM_ARRAY2, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 )
- (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp)
-{
- $1 = &data_temp;
- $2 = &dim1_temp;
- $3 = &dim2_temp;
-}
-%typemap(argout,
- fragment="NumPy_Backward_Compatibility")
- (DATA_TYPE** ARGOUTVIEWM_ARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2)
-{
- npy_intp dims[2] = { *$2, *$3 };
- PyObject* obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$1));
- PyArrayObject* array = (PyArrayObject*) obj;
-
- if (!array) SWIG_fail;
-
-%#ifdef SWIGPY_USE_CAPSULE
- PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
-%#else
- PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
-%#endif
-
-%#if NPY_API_VERSION < 0x00000007
- PyArray_BASE(array) = cap;
-%#else
- PyArray_SetBaseObject(array,cap);
-%#endif
-
- $result = SWIG_Python_AppendOutput($result,obj);
-}
-
-/* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEWM_ARRAY2)
- */
-%typemap(in,numinputs=0)
- (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DATA_TYPE** ARGOUTVIEWM_ARRAY2)
- (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DATA_TYPE* data_temp = NULL )
-{
- $1 = &dim1_temp;
- $2 = &dim2_temp;
- $3 = &data_temp;
-}
-%typemap(argout,
- fragment="NumPy_Backward_Compatibility")
- (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEWM_ARRAY2)
-{
- npy_intp dims[2] = { *$1, *$2 };
- PyObject* obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$3));
- PyArrayObject* array = (PyArrayObject*) obj;
-
- if (!array) SWIG_fail;
-
-%#ifdef SWIGPY_USE_CAPSULE
- PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
-%#else
- PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
-%#endif
-
-%#if NPY_API_VERSION < 0x00000007
- PyArray_BASE(array) = cap;
-%#else
- PyArray_SetBaseObject(array,cap);
-%#endif
-
- $result = SWIG_Python_AppendOutput($result,obj);
-}
-
-/* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2)
- */
-%typemap(in,numinputs=0)
- (DATA_TYPE** ARGOUTVIEWM_FARRAY2, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 )
- (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp)
-{
- $1 = &data_temp;
- $2 = &dim1_temp;
- $3 = &dim2_temp;
-}
-%typemap(argout,
- fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements")
- (DATA_TYPE** ARGOUTVIEWM_FARRAY2, DIM_TYPE* DIM1, DIM_TYPE* DIM2)
-{
- npy_intp dims[2] = { *$2, *$3 };
- PyObject* obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$1));
- PyArrayObject* array = (PyArrayObject*) obj;
-
- if (!array || !require_fortran(array)) SWIG_fail;
-
-%#ifdef SWIGPY_USE_CAPSULE
- PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
-%#else
- PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
-%#endif
-
-%#if NPY_API_VERSION < 0x00000007
- PyArray_BASE(array) = cap;
-%#else
- PyArray_SetBaseObject(array,cap);
-%#endif
-
- $result = SWIG_Python_AppendOutput($result,obj);
-}
-
-/* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEWM_FARRAY2)
- */
-%typemap(in,numinputs=0)
- (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DATA_TYPE** ARGOUTVIEWM_FARRAY2)
- (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DATA_TYPE* data_temp = NULL )
-{
- $1 = &dim1_temp;
- $2 = &dim2_temp;
- $3 = &data_temp;
-}
-%typemap(argout,
- fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements")
- (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DATA_TYPE** ARGOUTVIEWM_FARRAY2)
-{
- npy_intp dims[2] = { *$1, *$2 };
- PyObject* obj = PyArray_SimpleNewFromData(2, dims, DATA_TYPECODE, (void*)(*$3));
- PyArrayObject* array = (PyArrayObject*) obj;
-
- if (!array || !require_fortran(array)) SWIG_fail;
-
-%#ifdef SWIGPY_USE_CAPSULE
- PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
-%#else
- PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
-%#endif
-
-%#if NPY_API_VERSION < 0x00000007
- PyArray_BASE(array) = cap;
-%#else
- PyArray_SetBaseObject(array,cap);
-%#endif
-
- $result = SWIG_Python_AppendOutput($result,obj);
-}
-
-/* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2,
- DIM_TYPE* DIM3)
- */
-%typemap(in,numinputs=0)
- (DATA_TYPE** ARGOUTVIEWM_ARRAY3, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 )
- (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp)
-{
- $1 = &data_temp;
- $2 = &dim1_temp;
- $3 = &dim2_temp;
- $4 = &dim3_temp;
-}
-%typemap(argout,
- fragment="NumPy_Backward_Compatibility")
- (DATA_TYPE** ARGOUTVIEWM_ARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3)
-{
- npy_intp dims[3] = { *$2, *$3, *$4 };
- PyObject* obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$1));
- PyArrayObject* array = (PyArrayObject*) obj;
-
- if (!array) SWIG_fail;
-
-%#ifdef SWIGPY_USE_CAPSULE
- PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
-%#else
- PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
-%#endif
-
-%#if NPY_API_VERSION < 0x00000007
- PyArray_BASE(array) = cap;
-%#else
- PyArray_SetBaseObject(array,cap);
-%#endif
-
- $result = SWIG_Python_AppendOutput($result,obj);
-}
-
-/* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3,
- DATA_TYPE** ARGOUTVIEWM_ARRAY3)
- */
-%typemap(in,numinputs=0)
- (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DATA_TYPE** ARGOUTVIEWM_ARRAY3)
- (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DATA_TYPE* data_temp = NULL )
-{
- $1 = &dim1_temp;
- $2 = &dim2_temp;
- $3 = &dim3_temp;
- $4 = &data_temp;
-}
-%typemap(argout,
- fragment="NumPy_Backward_Compatibility")
- (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEWM_ARRAY3)
-{
- npy_intp dims[3] = { *$1, *$2, *$3 };
- PyObject* obj= PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$4));
- PyArrayObject* array = (PyArrayObject*) obj;
-
- if (!array) SWIG_fail;
-
-%#ifdef SWIGPY_USE_CAPSULE
- PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
-%#else
- PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
-%#endif
-
-%#if NPY_API_VERSION < 0x00000007
- PyArray_BASE(array) = cap;
-%#else
- PyArray_SetBaseObject(array,cap);
-%#endif
-
- $result = SWIG_Python_AppendOutput($result,obj);
-}
-
-/* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2,
- DIM_TYPE* DIM3)
- */
-%typemap(in,numinputs=0)
- (DATA_TYPE** ARGOUTVIEWM_FARRAY3, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 )
- (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp)
-{
- $1 = &data_temp;
- $2 = &dim1_temp;
- $3 = &dim2_temp;
- $4 = &dim3_temp;
-}
-%typemap(argout,
- fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements")
- (DATA_TYPE** ARGOUTVIEWM_FARRAY3, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3)
-{
- npy_intp dims[3] = { *$2, *$3, *$4 };
- PyObject* obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$1));
- PyArrayObject* array = (PyArrayObject*) obj;
-
- if (!array || !require_fortran(array)) SWIG_fail;
-
-%#ifdef SWIGPY_USE_CAPSULE
- PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
-%#else
- PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
-%#endif
-
-%#if NPY_API_VERSION < 0x00000007
- PyArray_BASE(array) = cap;
-%#else
- PyArray_SetBaseObject(array,cap);
-%#endif
-
- $result = SWIG_Python_AppendOutput($result,obj);
-}
-
-/* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3,
- DATA_TYPE** ARGOUTVIEWM_FARRAY3)
- */
-%typemap(in,numinputs=0)
- (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DATA_TYPE** ARGOUTVIEWM_FARRAY3)
- (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DATA_TYPE* data_temp = NULL )
-{
- $1 = &dim1_temp;
- $2 = &dim2_temp;
- $3 = &dim3_temp;
- $4 = &data_temp;
-}
-%typemap(argout,
- fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements")
- (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DATA_TYPE** ARGOUTVIEWM_FARRAY3)
-{
- npy_intp dims[3] = { *$1, *$2, *$3 };
- PyObject* obj = PyArray_SimpleNewFromData(3, dims, DATA_TYPECODE, (void*)(*$4));
- PyArrayObject* array = (PyArrayObject*) obj;
-
- if (!array || !require_fortran(array)) SWIG_fail;
-
-%#ifdef SWIGPY_USE_CAPSULE
- PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
-%#else
- PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
-%#endif
-
-%#if NPY_API_VERSION < 0x00000007
- PyArray_BASE(array) = cap;
-%#else
- PyArray_SetBaseObject(array,cap);
-%#endif
-
- $result = SWIG_Python_AppendOutput($result,obj);
-}
-
-/* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2,
- DIM_TYPE* DIM3, DIM_TYPE* DIM4)
- */
-%typemap(in,numinputs=0)
- (DATA_TYPE** ARGOUTVIEWM_ARRAY4, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 )
- (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp)
-{
- $1 = &data_temp;
- $2 = &dim1_temp;
- $3 = &dim2_temp;
- $4 = &dim3_temp;
- $5 = &dim4_temp;
-}
-%typemap(argout,
- fragment="NumPy_Backward_Compatibility")
- (DATA_TYPE** ARGOUTVIEWM_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4)
-{
- npy_intp dims[4] = { *$2, *$3, *$4 , *$5 };
- PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$1));
- PyArrayObject* array = (PyArrayObject*) obj;
-
- if (!array) SWIG_fail;
-
-%#ifdef SWIGPY_USE_CAPSULE
- PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
-%#else
- PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
-%#endif
-
-%#if NPY_API_VERSION < 0x00000007
- PyArray_BASE(array) = cap;
-%#else
- PyArray_SetBaseObject(array,cap);
-%#endif
-
- $result = SWIG_Python_AppendOutput($result,obj);
-}
-
-/* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4,
- DATA_TYPE** ARGOUTVIEWM_ARRAY4)
- */
-%typemap(in,numinputs=0)
- (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 , DATA_TYPE** ARGOUTVIEWM_ARRAY4)
- (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp, DATA_TYPE* data_temp = NULL )
-{
- $1 = &dim1_temp;
- $2 = &dim2_temp;
- $3 = &dim3_temp;
- $4 = &dim4_temp;
- $5 = &data_temp;
-}
-%typemap(argout,
- fragment="NumPy_Backward_Compatibility")
- (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_ARRAY4)
-{
- npy_intp dims[4] = { *$1, *$2, *$3 , *$4 };
- PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$5));
- PyArrayObject* array = (PyArrayObject*) obj;
-
- if (!array) SWIG_fail;
-
-%#ifdef SWIGPY_USE_CAPSULE
- PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
-%#else
- PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
-%#endif
-
-%#if NPY_API_VERSION < 0x00000007
- PyArray_BASE(array) = cap;
-%#else
- PyArray_SetBaseObject(array,cap);
-%#endif
-
- $result = SWIG_Python_AppendOutput($result,obj);
-}
-
-/* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2,
- DIM_TYPE* DIM3, DIM_TYPE* DIM4)
- */
-%typemap(in,numinputs=0)
- (DATA_TYPE** ARGOUTVIEWM_FARRAY4, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 )
- (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp)
-{
- $1 = &data_temp;
- $2 = &dim1_temp;
- $3 = &dim2_temp;
- $4 = &dim3_temp;
- $5 = &dim4_temp;
-}
-%typemap(argout,
- fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements")
- (DATA_TYPE** ARGOUTVIEWM_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3)
-{
- npy_intp dims[4] = { *$2, *$3, *$4 , *$5 };
- PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$1));
- PyArrayObject* array = (PyArrayObject*) obj;
-
- if (!array || !require_fortran(array)) SWIG_fail;
-
-%#ifdef SWIGPY_USE_CAPSULE
- PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
-%#else
- PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
-%#endif
-
-%#if NPY_API_VERSION < 0x00000007
- PyArray_BASE(array) = cap;
-%#else
- PyArray_SetBaseObject(array,cap);
-%#endif
-
- $result = SWIG_Python_AppendOutput($result,obj);
-}
-
-/* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4,
- DATA_TYPE** ARGOUTVIEWM_FARRAY4)
- */
-%typemap(in,numinputs=0)
- (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 , DATA_TYPE** ARGOUTVIEWM_FARRAY4)
- (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp, DATA_TYPE* data_temp = NULL )
-{
- $1 = &dim1_temp;
- $2 = &dim2_temp;
- $3 = &dim3_temp;
- $4 = &dim4_temp;
- $5 = &data_temp;
-}
-%typemap(argout,
- fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements")
- (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_FARRAY4)
-{
- npy_intp dims[4] = { *$1, *$2, *$3 , *$4 };
- PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$5));
- PyArrayObject* array = (PyArrayObject*) obj;
-
- if (!array || !require_fortran(array)) SWIG_fail;
-
-%#ifdef SWIGPY_USE_CAPSULE
- PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
-%#else
- PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
-%#endif
-
-%#if NPY_API_VERSION < 0x00000007
- PyArray_BASE(array) = cap;
-%#else
- PyArray_SetBaseObject(array,cap);
-%#endif
-
- $result = SWIG_Python_AppendOutput($result,obj);
-}
-
-/* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2,
- DIM_TYPE* DIM3, DIM_TYPE* DIM4)
- */
-%typemap(in,numinputs=0)
- (DATA_TYPE** ARGOUTVIEWM_ARRAY4, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 )
- (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp)
-{
- $1 = &data_temp;
- $2 = &dim1_temp;
- $3 = &dim2_temp;
- $4 = &dim3_temp;
- $5 = &dim4_temp;
-}
-%typemap(argout,
- fragment="NumPy_Backward_Compatibility")
- (DATA_TYPE** ARGOUTVIEWM_ARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4)
-{
- npy_intp dims[4] = { *$2, *$3, *$4 , *$5 };
- PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$1));
- PyArrayObject* array = (PyArrayObject*) obj;
-
- if (!array) SWIG_fail;
-
-%#ifdef SWIGPY_USE_CAPSULE
- PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
-%#else
- PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
-%#endif
-
-%#if NPY_API_VERSION < 0x00000007
- PyArray_BASE(array) = cap;
-%#else
- PyArray_SetBaseObject(array,cap);
-%#endif
-
- $result = SWIG_Python_AppendOutput($result,obj);
-}
-
-/* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4,
- DATA_TYPE** ARGOUTVIEWM_ARRAY4)
- */
-%typemap(in,numinputs=0)
- (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 , DATA_TYPE** ARGOUTVIEWM_ARRAY4)
- (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp, DATA_TYPE* data_temp = NULL )
-{
- $1 = &dim1_temp;
- $2 = &dim2_temp;
- $3 = &dim3_temp;
- $4 = &dim4_temp;
- $5 = &data_temp;
-}
-%typemap(argout,
- fragment="NumPy_Backward_Compatibility")
- (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_ARRAY4)
-{
- npy_intp dims[4] = { *$1, *$2, *$3 , *$4 };
- PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$5));
- PyArrayObject* array = (PyArrayObject*) obj;
-
- if (!array) SWIG_fail;
-
-%#ifdef SWIGPY_USE_CAPSULE
- PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
-%#else
- PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
-%#endif
-
-%#if NPY_API_VERSION < 0x00000007
- PyArray_BASE(array) = cap;
-%#else
- PyArray_SetBaseObject(array,cap);
-%#endif
-
- $result = SWIG_Python_AppendOutput($result,obj);
-}
-
-/* Typemap suite for (DATA_TYPE** ARGOUTVIEWM_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2,
- DIM_TYPE* DIM3, DIM_TYPE* DIM4)
- */
-%typemap(in,numinputs=0)
- (DATA_TYPE** ARGOUTVIEWM_FARRAY4, DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 )
- (DATA_TYPE* data_temp = NULL , DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp)
-{
- $1 = &data_temp;
- $2 = &dim1_temp;
- $3 = &dim2_temp;
- $4 = &dim3_temp;
- $5 = &dim4_temp;
-}
-%typemap(argout,
- fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements")
- (DATA_TYPE** ARGOUTVIEWM_FARRAY4, DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4)
-{
- npy_intp dims[4] = { *$2, *$3, *$4 , *$5 };
- PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$1));
- PyArrayObject* array = (PyArrayObject*) obj;
-
- if (!array || !require_fortran(array)) SWIG_fail;
-
-%#ifdef SWIGPY_USE_CAPSULE
- PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
-%#else
- PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
-%#endif
-
-%#if NPY_API_VERSION < 0x00000007
- PyArray_BASE(array) = cap;
-%#else
- PyArray_SetBaseObject(array,cap);
-%#endif
-
- $result = SWIG_Python_AppendOutput($result,obj);
-}
-
-/* Typemap suite for (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4,
- DATA_TYPE** ARGOUTVIEWM_FARRAY4)
- */
-%typemap(in,numinputs=0)
- (DIM_TYPE* DIM1 , DIM_TYPE* DIM2 , DIM_TYPE* DIM3 , DIM_TYPE* DIM4 , DATA_TYPE** ARGOUTVIEWM_FARRAY4)
- (DIM_TYPE dim1_temp, DIM_TYPE dim2_temp, DIM_TYPE dim3_temp, DIM_TYPE dim4_temp, DATA_TYPE* data_temp = NULL )
-{
- $1 = &dim1_temp;
- $2 = &dim2_temp;
- $3 = &dim3_temp;
- $4 = &dim4_temp;
- $5 = &data_temp;
-}
-%typemap(argout,
- fragment="NumPy_Backward_Compatibility,NumPy_Array_Requirements")
- (DIM_TYPE* DIM1, DIM_TYPE* DIM2, DIM_TYPE* DIM3, DIM_TYPE* DIM4, DATA_TYPE** ARGOUTVIEWM_FARRAY4)
-{
- npy_intp dims[4] = { *$1, *$2, *$3 , *$4 };
- PyObject* obj = PyArray_SimpleNewFromData(4, dims, DATA_TYPECODE, (void*)(*$5));
- PyArrayObject* array = (PyArrayObject*) obj;
-
- if (!array || !require_fortran(array)) SWIG_fail;
-
-%#ifdef SWIGPY_USE_CAPSULE
- PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME, free_cap);
-%#else
- PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
-%#endif
-
-%#if NPY_API_VERSION < 0x00000007
- PyArray_BASE(array) = cap;
-%#else
- PyArray_SetBaseObject(array,cap);
-%#endif
-
- $result = SWIG_Python_AppendOutput($result,obj);
-}
-
-%enddef /* %numpy_typemaps() macro */
-/* *************************************************************** */
-
-/* Concrete instances of the %numpy_typemaps() macro: Each invocation
- * below applies all of the typemaps above to the specified data type.
- */
-%numpy_typemaps(signed char , NPY_BYTE , int)
-%numpy_typemaps(unsigned char , NPY_UBYTE , int)
-%numpy_typemaps(short , NPY_SHORT , int)
-%numpy_typemaps(unsigned short , NPY_USHORT , int)
-%numpy_typemaps(int , NPY_INT , int)
-%numpy_typemaps(unsigned int , NPY_UINT , int)
-%numpy_typemaps(long , NPY_LONG , int)
-%numpy_typemaps(unsigned long , NPY_ULONG , int)
-%numpy_typemaps(long long , NPY_LONGLONG , int)
-%numpy_typemaps(unsigned long long, NPY_ULONGLONG, int)
-%numpy_typemaps(float , NPY_FLOAT , int)
-%numpy_typemaps(double , NPY_DOUBLE , int)
-
-/* ***************************************************************
- * The follow macro expansion does not work, because C++ bool is 4
- * bytes and NPY_BOOL is 1 byte
- *
- * %numpy_typemaps(bool, NPY_BOOL, int)
- */
-
-/* ***************************************************************
- * On my Mac, I get the following warning for this macro expansion:
- * 'swig/python detected a memory leak of type 'long double *', no destructor found.'
- *
- * %numpy_typemaps(long double, NPY_LONGDOUBLE, int)
- */
-
-/* ***************************************************************
- * Swig complains about a syntax error for the following macro
- * expansions:
- *
- * %numpy_typemaps(complex float, NPY_CFLOAT , int)
- *
- * %numpy_typemaps(complex double, NPY_CDOUBLE, int)
- *
- * %numpy_typemaps(complex long double, NPY_CLONGDOUBLE, int)
- */
-
-#endif /* SWIGPYTHON */
diff --git a/swig/start.i b/swig/start.i
deleted file mode 100644
index 24aa1a26a..000000000
--- a/swig/start.i
+++ /dev/null
@@ -1,14 +0,0 @@
-%{
-#define SWIG_FILE_WITH_INIT
-#include <assert.h>
-%}
-// numpy macros
-%include numpy.i
-
-%init %{
- import_array();
-%}
-
-
-//usefull macros used to instanciate templates
-%define INSTANTIATED_DIMENSIONS 1,2,3,4,5,6,7 %enddef
--
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