Commit 7a3c6167 authored by Santiago Ospina De Los Ríos's avatar Santiago Ospina De Los Ríos

Merge branch '191-remove-superfluous-folders-and-files' into 'master'

Resolve "Remove superfluous folders and files"

Closes #191

See merge request !200
parents bd3fcafc ff1cc35f
......@@ -43,12 +43,10 @@ set(DORIE_EXE_PATH ${PROJECT_BINARY_DIR}/dune/dorie/
# add subdirectories
add_subdirectory("plugins/vendor")
add_subdirectory("m4")
add_subdirectory("cmake/modules")
add_subdirectory("python")
add_subdirectory("doc")
add_subdirectory("dune")
add_subdirectory("lib")
if(DORIE_TESTING)
add_subdirectory("test")
add_subdirectory("doc/cookbook")
......
MODULES = DorieMacros.cmake
modulesdir = $(datadir)/dune/cmake/modules
dist_modules_DATA = ${MODULES}
include $(top_srcdir)/am/global-rules
EXTRA_DIST = CMakeLists.txt
BASEDIR=../..
CURDIR=doc/doxygen
EXTRA_DIST = CMakeLists.txt
include $(top_srcdir)/am/doxygen
include $(top_srcdir)/am/global-rules
install(PROGRAMS dorie.m4 DESTINATION ${CMAKE_INSTALL_DATAROOTDIR}/dune/aclocal)
dnl -*- autoconf -*-
# Macros needed to find dorie and dependent libraries. They are called by
# the macros in ${top_src_dir}/dependencies.m4, which is generated by
# "dunecontrol autogen"
# Additional checks needed to build dorie
# This macro should be invoked by every module which depends on dorie, as
# well as by dorie itself
AC_DEFUN([DORIE_CHECKS])
# Additional checks needed to find dorie
# This macro should be invoked by every module which depends on dorie, but
# not by dorie itself
AC_DEFUN([DORIE_CHECK_MODULE],
[
DUNE_CHECK_MODULES([dorie],[dorie/dorie.hh])
])
......@@ -26,11 +26,6 @@ add_custom_target(test_run_ref
COMMAND ctest --output-on-failure --tests-regex ^.+_ref_.+$
)
# dorie_add_metaini_test(TARGET dorie_test METAINI reference_2d.mini.in)
# dorie_add_metaini_test(TARGET dorie_test METAINI reference_3d.mini.in)
# dorie_add_metaini_test(TARGET dorie_test METAINI reference_evaporation.mini.in)
# dorie_add_metaini_test(TARGET dorie_test METAINI reference_interpolators.mini.in)
# dorie in parallel
dorie_add_metaini_test(TARGET dorie_test METAINI parallel_reference.mini.in)
dorie_add_metaini_test(TARGET dorie_test METAINI parallel_reference_compare.mini.in)
......
include ${CMAKE_BINARY_DIR}/doc/default_files/config.ini
__name = reference_2d
_test_command = run
_asset_path = "${CMAKE_CURRENT_LIST_DIR}"
_evaluation = reference
_reference.path = {_asset_path}/references/2d/
_reference.abstol = 1e-8
grid.initialLevel = 1
grid.gridType = gmsh, rectangular | expand
grid.cells = 20 20
grid.extensions = 1 1
grid.gridFile = "{_asset_path}/meshes/square.msh"
adaptivity.policy = none
adaptivity.threshold = 5E-8
adaptivity.refinementFraction = 0.05
adaptivity.maxLevel = 7
[richards]
output.fileName = reference_2d | unique name
output.outputPath = reference_2d | unique name
boundary.file = "{_asset_path}/bcs/infiltration_2d.dat"
parameters.arrayFile = "{_asset_path}/parfields/fft_2d.h5", "{_asset_path}/parfields/fft_2d_miller.h5" | expand distribution
time.end = 1E5
time.maxTimestep = 1E5
time.startTimestep = 1E3
include ${CMAKE_BINARY_DIR}/doc/default_files/config.ini
__name = reference_3d
_test_command = run
_asset_path = "${CMAKE_CURRENT_LIST_DIR}"
_evaluation = reference
_reference.path = {_asset_path}/references/3d/
grid.dimensions = 3
grid.initialLevel = 1
grid.gridType = gmsh
grid.extensions = 1 1 1
grid.gridFile = "{_asset_path}/meshes/3dcube.msh"
adaptivity.policy = none
adaptivity.threshold = 1E-6
[richards]
output.fileName = reference_3d | unique name
output.outputPath = reference_3d | unique name
boundary.file = "{_asset_path}/bcs/infiltration_3d.dat"
parameters.arrayFile = "{_asset_path}/parfields/fft_3d.h5", "{_asset_path}/parfields/fft_3d_miller.h5" | expand
parameters.scale = 1 1 1
parameters.offset = 0 0 0
parameters.interpolation = linear
time.end = 1E5
time.maxTimestep = 1E5
time.startTimestep = 1E4
include ${CMAKE_BINARY_DIR}/doc/default_files/config.ini
__name = reference_evaporation
_test_command = run
_asset_path = "${CMAKE_CURRENT_LIST_DIR}"
_evaluation = reference
_reference.path = {_asset_path}/references/evaporation/
_reference.abstol = 1e-8
grid.initialLevel = 2
grid.gridType = gmsh, rectangular | expand
grid.cells = 20 20
grid.extensions = 1 1
grid.gridFile = "{_asset_path}/meshes/square.msh"
adaptivity.policy = none
adaptivity.threshold = 1E-7
[richards]
output.fileName = reference_evaporation | unique name
output.outputPath = reference_evaporation | unique name
boundary.file = "{_asset_path}/bcs/infiltration_evaporation_2d.dat"
parameters.arrayFile = "{_asset_path}/parfields/fft_2d.h5"
time.end = 1E6
time.maxTimestep = 1E6
time.startTimestep = 1E3
\ No newline at end of file
include ${CMAKE_BINARY_DIR}/doc/default_files/config.ini
__name = reference_interpolators
_test_command = run
_asset_path = "${CMAKE_CURRENT_LIST_DIR}"
_evaluation = reference
_reference.path = {_asset_path}/references/interpolators/
_reference.abstol = 0.05
grid.initialLevel = 2
grid.gridType = gmsh, rectangular | expand
grid.cells = 20 20
grid.extensions = 1 1
grid.gridFile = "{_asset_path}/meshes/square.msh"
adaptivity.policy = waterFlux
adaptivity.threshold = 7E-8
[richards]
output.fileName = reference_interpolators | unique name
output.outputPath = reference_interpolators | unique name
boundary.file = "{_asset_path}/bcs/infiltration_2d.dat"
parameters.arrayFile = "{_asset_path}/parfields/fft_2d_miller.h5"
parameters.interpolation = linear, cubic | expand
time.end = 1E5
time.maxTimestep = 1E5
time.startTimestep = 1E3
\ No newline at end of file
<?xml version="1.0"?>
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This source diff could not be displayed because it is too large. You can view the blob instead.
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