From ac898be8885b899de7783fd22cfae1c15114035d Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Fri, 22 Sep 2023 20:24:58 +0200 Subject: [PATCH 01/84] fix issues with compilation preprocessor statement or unsupported private/public statemens for modules seem to confuse CMake --- src/math.f90 | 9 --------- src/phase.f90 | 15 --------------- 2 files changed, 24 deletions(-) diff --git a/src/math.f90 b/src/math.f90 index 24141f4e9..be66fdffb 100644 --- a/src/math.f90 +++ b/src/math.f90 @@ -30,15 +30,6 @@ module math module procedure math_expand_real end interface math_expand -#if __INTEL_COMPILER >= 1900 - ! do not make use of associated entities available to other modules - private :: & - misc, & - IO, & - config, & - parallelization -#endif - real(pREAL), parameter :: & PI = acos(-1.0_pREAL), & !< ratio of a circle's circumference to its diameter TAU = 2.0_pREAL*PI, & !< ratio of a circle's circumference to its radius diff --git a/src/phase.f90 b/src/phase.f90 index 4cc341913..ffa606cc2 100644 --- a/src/phase.f90 +++ b/src/phase.f90 @@ -353,21 +353,6 @@ module phase end interface - -#if __INTEL_COMPILER >= 1900 - public :: & - prec, & - math, & - rotations, & - IO, & - config, & - material, & - result, & - crystal, & - discretization, & - HDF5_utilities -#endif - public :: & phase_init, & phase_homogenizedC66, & From 0e353d9febe4fed92a9fa223a1994e90e6f2c9c1 Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Wed, 4 Oct 2023 21:59:20 +0200 Subject: [PATCH 02/84] no merge in hot loop results in measureable run time decrease for Intel and GNU compilers --- src/phase_mechanical_plastic_dislotungsten.f90 | 7 ++++--- src/phase_mechanical_plastic_kinehardening.f90 | 7 ++++--- src/phase_mechanical_plastic_phenopowerlaw.f90 | 7 ++++--- 3 files changed, 12 insertions(+), 9 deletions(-) diff --git a/src/phase_mechanical_plastic_dislotungsten.f90 b/src/phase_mechanical_plastic_dislotungsten.f90 index 2d3da1534..ad9853b3a 100644 --- a/src/phase_mechanical_plastic_dislotungsten.f90 +++ b/src/phase_mechanical_plastic_dislotungsten.f90 @@ -295,6 +295,8 @@ pure module subroutine dislotungsten_LpAndItsTangent(Lp,dLp_dMp, & T !< temperature real(pREAL), dimension(param(ph)%sum_N_sl) :: & dot_gamma, ddot_gamma_dtau + real(pREAL), dimension(3,3,param(ph)%sum_N_sl) :: & + P_nS T = thermal_T(ph,en) @@ -304,13 +306,12 @@ pure module subroutine dislotungsten_LpAndItsTangent(Lp,dLp_dMp, & associate(prm => param(ph)) call kinetics(Mp,T,ph,en, dot_gamma,ddot_gamma_dtau) + P_nS = merge(prm%P_nS_pos,prm%P_nS_neg, spread(spread(dot_gamma,1,3),2,3)>0.0_pREAL) ! faster than 'merge' in loop do i = 1, prm%sum_N_sl Lp = Lp + dot_gamma(i)*prm%P_sl(1:3,1:3,i) forall (k=1:3,l=1:3,m=1:3,n=1:3) & dLp_dMp(k,l,m,n) = dLp_dMp(k,l,m,n) & - + ddot_gamma_dtau(i) * prm%P_sl(k,l,i) & - * merge(prm%P_nS_pos(m,n,i), & - prm%P_nS_neg(m,n,i), dot_gamma(i)>0.0_pREAL) + + ddot_gamma_dtau(i) * prm%P_sl(k,l,i) * P_nS(m,n,i) end do end associate diff --git a/src/phase_mechanical_plastic_kinehardening.f90 b/src/phase_mechanical_plastic_kinehardening.f90 index 549fd15ef..36cc8a37a 100644 --- a/src/phase_mechanical_plastic_kinehardening.f90 +++ b/src/phase_mechanical_plastic_kinehardening.f90 @@ -272,6 +272,8 @@ pure module subroutine kinehardening_LpAndItsTangent(Lp,dLp_dMp, Mp,ph,en) i,k,l,m,n real(pREAL), dimension(param(ph)%sum_N_sl) :: & dot_gamma, ddot_gamma_dtau + real(pREAL), dimension(3,3,param(ph)%sum_N_sl) :: & + P_nS Lp = 0.0_pREAL @@ -280,13 +282,12 @@ pure module subroutine kinehardening_LpAndItsTangent(Lp,dLp_dMp, Mp,ph,en) associate(prm => param(ph)) call kinetics(Mp,ph,en, dot_gamma,ddot_gamma_dtau) + P_nS = merge(prm%P_nS_pos,prm%P_nS_neg, spread(spread(dot_gamma,1,3),2,3)>0.0_pREAL) ! faster than 'merge' in loop do i = 1, prm%sum_N_sl Lp = Lp + dot_gamma(i)*prm%P(1:3,1:3,i) forall (k=1:3,l=1:3,m=1:3,n=1:3) & dLp_dMp(k,l,m,n) = dLp_dMp(k,l,m,n) & - + ddot_gamma_dtau(i) * prm%P(k,l,i) & - * merge(prm%P_nS_pos(m,n,i), & - prm%P_nS_neg(m,n,i), dot_gamma(i)>0.0_pREAL) + + ddot_gamma_dtau(i) * prm%P(k,l,i) * P_nS(m,n,i) end do end associate diff --git a/src/phase_mechanical_plastic_phenopowerlaw.f90 b/src/phase_mechanical_plastic_phenopowerlaw.f90 index 77c6db4dd..94d00d342 100644 --- a/src/phase_mechanical_plastic_phenopowerlaw.f90 +++ b/src/phase_mechanical_plastic_phenopowerlaw.f90 @@ -312,6 +312,8 @@ pure module subroutine phenopowerlaw_LpAndItsTangent(Lp,dLp_dMp,Mp,ph,en) i,k,l,m,n real(pREAL), dimension(param(ph)%sum_N_sl) :: & dot_gamma_sl,ddot_gamma_dtau_sl + real(pREAL), dimension(3,3,param(ph)%sum_N_sl) :: & + P_nS real(pREAL), dimension(param(ph)%sum_N_tw) :: & dot_gamma_tw,ddot_gamma_dtau_tw @@ -322,13 +324,12 @@ pure module subroutine phenopowerlaw_LpAndItsTangent(Lp,dLp_dMp,Mp,ph,en) associate(prm => param(ph)) call kinetics_sl(Mp,ph,en,dot_gamma_sl,ddot_gamma_dtau_sl) + P_nS = merge(prm%P_nS_pos,prm%P_nS_neg, spread(spread(dot_gamma_sl,1,3),2,3)>0.0_pREAL) ! faster than 'merge' in loop slipSystems: do i = 1, prm%sum_N_sl Lp = Lp + dot_gamma_sl(i)*prm%P_sl(1:3,1:3,i) forall (k=1:3,l=1:3,m=1:3,n=1:3) & dLp_dMp(k,l,m,n) = dLp_dMp(k,l,m,n) & - + ddot_gamma_dtau_sl(i) * prm%P_sl(k,l,i) & - * merge(prm%P_nS_pos(m,n,i), & - prm%P_nS_neg(m,n,i), dot_gamma_sl(i)>0.0_pREAL) + + ddot_gamma_dtau_sl(i) * prm%P_sl(k,l,i) * P_nS(m,n,i) end do slipSystems call kinetics_tw(Mp,ph,en,dot_gamma_tw,ddot_gamma_dtau_tw) From 2e153086c9606a22aa5599b5b4b071603a345bf0 Mon Sep 17 00:00:00 2001 From: Test User Date: Fri, 13 Oct 2023 16:41:08 +0200 Subject: [PATCH 03/84] [skip ci] updated version information after successful test of v3.0.0-alpha7-883-g3517c7dee --- VERSION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/VERSION b/VERSION index 2dfbe91c2..ecf7be2ae 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -3.0.0-alpha7-880-g3644fe586 +3.0.0-alpha7-883-g3517c7dee From 467638e95216de761fd89b73b70db1ae7cfdb15e Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Thu, 12 Oct 2023 23:21:40 +0200 Subject: [PATCH 04/84] do not include origin into calculation --- python/damask/_grid.py | 9 ++++++--- python/damask/seeds.py | 10 ++++++++-- 2 files changed, 14 insertions(+), 5 deletions(-) diff --git a/python/damask/_grid.py b/python/damask/_grid.py index a8df4f877..935a7f767 100644 --- a/python/damask/_grid.py +++ b/python/damask/_grid.py @@ -487,9 +487,11 @@ class Grid: size : sequence of float, len (3) Edge lengths of the grid in meter. seeds : numpy.ndarray of float, shape (:,3) - Position of the seed points in meter. All points need to lay within the box. + Position of the seed points in meter. All points need + to lay within the box [(0,0,0),size]. weights : sequence of float, len (seeds.shape[0]) - Weights of the seeds. Setting all weights to 1.0 gives a standard Voronoi tessellation. + Weights of the seeds. Setting all weights to 1.0 gives a + standard Voronoi tessellation. material : sequence of int, len (seeds.shape[0]), optional Material ID of the seeds. Defaults to None, in which case materials are consecutively numbered. @@ -544,7 +546,8 @@ class Grid: size : sequence of float, len (3) Edge lengths of the grid in meter. seeds : numpy.ndarray of float, shape (:,3) - Position of the seed points in meter. All points need to lay within the box. + Position of the seed points in meter. All points need + to lay within the box [(0,0,0),size]. material : sequence of int, len (seeds.shape[0]), optional Material ID of the seeds. Defaults to None, in which case materials are consecutively numbered. diff --git a/python/damask/seeds.py b/python/damask/seeds.py index 808ad3c1b..e80425a29 100644 --- a/python/damask/seeds.py +++ b/python/damask/seeds.py @@ -134,6 +134,12 @@ def from_grid(grid, coords, materials : numpy.ndarray, shape (:,3); numpy.ndarray, shape (:) Seed coordinates in 3D space, material IDs. + Notes + ----- + The origin is not considered in order to obtain coordinates + in a coordinate system located at the origin. This is expected + by damask.Grid.from_Voronoi_tessellation. + Examples -------- Recreate seeds from Voronoi tessellation. @@ -166,8 +172,8 @@ def from_grid(grid, materials = _np.unique(material[mask]) coords_ = _np.zeros((materials.size,3),dtype=float) for i,mat in enumerate(materials): - pc = (2*_np.pi*coords[material[:,0]==mat,:]-grid.origin)/grid.size - coords_[i] = grid.origin + grid.size / 2 / _np.pi * (_np.pi + + pc = 2*_np.pi*coords[material[:,0]==mat,:]/grid.size + coords_[i] = grid.size / 2 / _np.pi * (_np.pi + _np.arctan2(-_np.average(_np.sin(pc),axis=0), -_np.average(_np.cos(pc),axis=0))) \ if periodic else \ From 0bcea6d9b0e5464f94f6207f5074e0d7e0c19f58 Mon Sep 17 00:00:00 2001 From: Test User Date: Mon, 16 Oct 2023 18:59:38 +0200 Subject: [PATCH 05/84] [skip ci] updated version information after successful test of v3.0.0-alpha7-888-g5bb7e54df --- VERSION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/VERSION b/VERSION index ecf7be2ae..c62aab3b8 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -3.0.0-alpha7-883-g3517c7dee +3.0.0-alpha7-888-g5bb7e54df From e5864efb8a64c10403ab298042437f9aabc2edac Mon Sep 17 00:00:00 2001 From: Franz Roters Date: Wed, 18 Oct 2023 13:53:23 +0200 Subject: [PATCH 06/84] add support for Marc2023.2 --- .../2023.2/Marc_tools/comp_damask_hmp.patch | 49 ++ .../2023.2/Marc_tools/comp_damask_lmp.patch | 49 ++ .../2023.2/Marc_tools/comp_damask_mp.patch | 49 ++ .../2023.2/Marc_tools/include_linux64.patch | 75 +++ .../2023.2/Marc_tools/run_damask_hmp.patch | 517 ++++++++++++++++++ .../2023.2/Marc_tools/run_damask_lmp.patch | 517 ++++++++++++++++++ .../2023.2/Marc_tools/run_damask_mp.patch | 517 ++++++++++++++++++ .../2023.2/Mentat_bin/edit_window.patch | 24 + .../MarcMentat/2023.2/Mentat_bin/kill4.patch | 0 .../MarcMentat/2023.2/Mentat_bin/kill5.patch | 0 .../MarcMentat/2023.2/Mentat_bin/kill6.patch | 0 .../2023.2/Mentat_bin/submit4.patch | 38 ++ .../2023.2/Mentat_bin/submit5.patch | 38 ++ .../2023.2/Mentat_bin/submit6.patch | 38 ++ .../2023.2/Mentat_menus/job_run.ms.patch | 158 ++++++ src/Marc/include/concom2023.2 | 473 ++++++++++++++++ src/Marc/include/creeps2023.2 | 73 +++ 17 files changed, 2615 insertions(+) create mode 100644 install/MarcMentat/2023.2/Marc_tools/comp_damask_hmp.patch create mode 100644 install/MarcMentat/2023.2/Marc_tools/comp_damask_lmp.patch create mode 100644 install/MarcMentat/2023.2/Marc_tools/comp_damask_mp.patch create mode 100644 install/MarcMentat/2023.2/Marc_tools/include_linux64.patch create mode 100644 install/MarcMentat/2023.2/Marc_tools/run_damask_hmp.patch create mode 100644 install/MarcMentat/2023.2/Marc_tools/run_damask_lmp.patch create mode 100644 install/MarcMentat/2023.2/Marc_tools/run_damask_mp.patch create mode 100644 install/MarcMentat/2023.2/Mentat_bin/edit_window.patch create mode 100644 install/MarcMentat/2023.2/Mentat_bin/kill4.patch create mode 100644 install/MarcMentat/2023.2/Mentat_bin/kill5.patch create mode 100644 install/MarcMentat/2023.2/Mentat_bin/kill6.patch create mode 100644 install/MarcMentat/2023.2/Mentat_bin/submit4.patch create mode 100644 install/MarcMentat/2023.2/Mentat_bin/submit5.patch create mode 100644 install/MarcMentat/2023.2/Mentat_bin/submit6.patch create mode 100644 install/MarcMentat/2023.2/Mentat_menus/job_run.ms.patch create mode 100644 src/Marc/include/concom2023.2 create mode 100644 src/Marc/include/creeps2023.2 diff --git a/install/MarcMentat/2023.2/Marc_tools/comp_damask_hmp.patch b/install/MarcMentat/2023.2/Marc_tools/comp_damask_hmp.patch new file mode 100644 index 000000000..886ebf008 --- /dev/null +++ b/install/MarcMentat/2023.2/Marc_tools/comp_damask_hmp.patch @@ -0,0 +1,49 @@ +--- ++++ +@@ -6,18 +6,27 @@ + DIR=$1 + user=$3 + program=$4 ++usernoext=$user ++usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f` ++usernoext=`dirname $usernoext`/`$BASENAME $usernoext .F` ++usernoext=`dirname $usernoext`/`$BASENAME $usernoext .for` ++usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f90` ++ ++# add BLAS options for linking ++ BLAS="%BLAS%" ++ + . $DIR/tools/include + DIRJOB=$2 + cd $DIRJOB +-echo "Compiling and linking user subroutine $user.f on host `hostname`" ++echo "Compiling and linking user subroutine $user on host `hostname`" + echo "program: $program" +- $FORTRAN $user.f || \ ++ $DFORTHIGHMP $user || \ + { +- echo "$0: compile failed for $user.f" ++ echo "$0: compile failed for $user" + exit 1 + } + /bin/rm $program 2>/dev/null +- userobj=$user.o ++ userobj=$usernoext.o + + + $LOAD ${program} $DIR/lib/main.o\ +@@ -33,9 +42,13 @@ + $TKLIBS \ + $MRCLIBS \ + $METISLIBS \ ++ $BLAS \ + $SYSLIBS || \ + { +- echo "$0: link failed for $user.o on host `hostname`" ++ echo "$0: link failed for $usernoext.o on host `hostname`" + exit 1 + } + /bin/rm $userobj ++ /bin/rm $DIRJOB/*.mod ++ /bin/rm $DIRJOB/*.smod ++ /bin/rm $DIRJOB/*_genmod.f90 diff --git a/install/MarcMentat/2023.2/Marc_tools/comp_damask_lmp.patch b/install/MarcMentat/2023.2/Marc_tools/comp_damask_lmp.patch new file mode 100644 index 000000000..191cb1a53 --- /dev/null +++ b/install/MarcMentat/2023.2/Marc_tools/comp_damask_lmp.patch @@ -0,0 +1,49 @@ +--- ++++ +@@ -6,18 +6,27 @@ + DIR=$1 + user=$3 + program=$4 ++usernoext=$user ++usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f` ++usernoext=`dirname $usernoext`/`$BASENAME $usernoext .F` ++usernoext=`dirname $usernoext`/`$BASENAME $usernoext .for` ++usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f90` ++ ++# add BLAS options for linking ++ BLAS="%BLAS%" ++ + . $DIR/tools/include + DIRJOB=$2 + cd $DIRJOB +-echo "Compiling and linking user subroutine $user.f on host `hostname`" ++echo "Compiling and linking user subroutine $user on host `hostname`" + echo "program: $program" +- $FORTRAN $user.f || \ ++ $DFORTRANLOWMP $user || \ + { +- echo "$0: compile failed for $user.f" ++ echo "$0: compile failed for $user" + exit 1 + } + /bin/rm $program 2>/dev/null +- userobj=$user.o ++ userobj=$usernoext.o + + + $LOAD ${program} $DIR/lib/main.o\ +@@ -33,9 +42,13 @@ + $TKLIBS \ + $MRCLIBS \ + $METISLIBS \ ++ $BLAS \ + $SYSLIBS || \ + { +- echo "$0: link failed for $user.o on host `hostname`" ++ echo "$0: link failed for $usernoext.o on host `hostname`" + exit 1 + } + /bin/rm $userobj ++ /bin/rm $DIRJOB/*.mod ++ /bin/rm $DIRJOB/*.smod ++ /bin/rm $DIRJOB/*_genmod.f90 diff --git a/install/MarcMentat/2023.2/Marc_tools/comp_damask_mp.patch b/install/MarcMentat/2023.2/Marc_tools/comp_damask_mp.patch new file mode 100644 index 000000000..7c9cf7ba7 --- /dev/null +++ b/install/MarcMentat/2023.2/Marc_tools/comp_damask_mp.patch @@ -0,0 +1,49 @@ +--- ++++ +@@ -6,18 +6,27 @@ + DIR=$1 + user=$3 + program=$4 ++usernoext=$user ++usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f` ++usernoext=`dirname $usernoext`/`$BASENAME $usernoext .F` ++usernoext=`dirname $usernoext`/`$BASENAME $usernoext .for` ++usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f90` ++ ++# add BLAS options for linking ++ BLAS="%BLAS%" ++ + . $DIR/tools/include + DIRJOB=$2 + cd $DIRJOB +-echo "Compiling and linking user subroutine $user.f on host `hostname`" ++echo "Compiling and linking user subroutine $user on host `hostname`" + echo "program: $program" +- $FORTRAN $user.f || \ ++ $DFORTRANMP $user || \ + { +- echo "$0: compile failed for $user.f" ++ echo "$0: compile failed for $user" + exit 1 + } + /bin/rm $program 2>/dev/null +- userobj=$user.o ++ userobj=$usernoext.o + + + $LOAD ${program} $DIR/lib/main.o\ +@@ -33,9 +42,13 @@ + $TKLIBS \ + $MRCLIBS \ + $METISLIBS \ ++ $BLAS \ + $SYSLIBS || \ + { +- echo "$0: link failed for $user.o on host `hostname`" ++ echo "$0: link failed for $usernoext.o on host `hostname`" + exit 1 + } + /bin/rm $userobj ++ /bin/rm $DIRJOB/*.mod ++ /bin/rm $DIRJOB/*.smod ++ /bin/rm $DIRJOB/*_genmod.f90 diff --git a/install/MarcMentat/2023.2/Marc_tools/include_linux64.patch b/install/MarcMentat/2023.2/Marc_tools/include_linux64.patch new file mode 100644 index 000000000..f9646b240 --- /dev/null +++ b/install/MarcMentat/2023.2/Marc_tools/include_linux64.patch @@ -0,0 +1,75 @@ +--- ++++ +@@ -172,6 +178,15 @@ + MARC_COSIM_LIB="$MSCCOSIM_HOME/CoSim$MSCCOSIM_VERSION/Dcosim$MSCCOSIM_VERSION/lib" + fi + ++# DAMASK uses the HDF5 compiler wrapper around the Intel compiler ++H5FC=$(h5fc -shlib -show) ++if [[ "$H5FC" == *"$dir is"* ]]; then ++ H5FC=$(echo $(echo "$H5FC" | tail -n1) | sed -e "s/\-shlib/-fPIC -qopenmp/g") ++ H5FC=${H5FC%-lmpifort*} ++fi ++HDF5_LIB=${H5FC//*ifort/} ++FCOMP="$H5FC" ++ + # AEM + if test "$MARCDLLOUTDIR" = ""; then + DLLOUTDIR="$MARC_LIB" +@@ -599,7 +609,7 @@ + PROFILE=" $PROFILE -pg" + fi + +-FORT_OPT="-c -assume byterecl -safe-cray-ptr -mp1 -WB -fp-model source" ++FORT_OPT="-c -implicitnone -stand f18 -standard-semantics -assume nostd_mod_proc_name -safe-cray-ptr -mp1 -WB -fp-model source" + if test "$MTHREAD" = "OPENMP" + then + FORT_OPT=" $FORT_OPT -qopenmp" +@@ -612,7 +621,7 @@ + FORT_OPT=" $FORT_OPT -save -zero" + fi + if test "$MARCHDF_HDF" = "HDF"; then +- FORT_OPT="$FORT_OPT -DMARCHDF_HDF=$MARCHDF_HDF $HDF_INCLUDE" ++ FORT_OPT="$FORT_OPT -DMARCHDF=$MARCHDF_HDF" + fi + + FORTLOW="$FCOMP $FORT_OPT $PROFILE -O0 $I8FFLAGS -I$MARC_SOURCE/common \ +@@ -626,6 +635,29 @@ + # for compiling free form f90 files. high opt, integer(4) + FORTF90="$FCOMP -c -O3" + ++# determine DAMASK version ++if test -n "$DAMASK_USER"; then ++ DAMASKROOT=`dirname $DAMASK_USER`/../.. ++ read DAMASKVERSION < $DAMASKROOT/VERSION ++ DAMASKVERSION="'"$DAMASKVERSION"'" ++else ++ DAMASKVERSION="'N/A'" ++fi ++ ++# DAMASK compiler calls ++DFORTLOWMP="$FCOMP -c -O0 -qno-offload -implicitnone -stand f18 -standard-semantics -assume nostd_mod_proc_name -safe-cray-ptr $PROFILE -zero -mp1 -WB $I8FFLAGS -I$MARC_SOURCE/common \ ++ -fpp -ftz -diag-disable 5268 -warn declarations -warn general -warn usage -warn interfaces -warn ignore_loc -warn alignments -DMARC4DAMASK=2023.2 -DDAMASKVERSION=$DAMASKVERSION \ ++ -qopenmp -qopenmp-threadprivate=compat\ ++ $MUMPS_INCLUDE $I8DEFINES -DLinux -DLINUX -DLinux_intel $FDEFINES $DDM $SOLVERFLAGS -I$KDTREE2_MOD -I$MARC_MOD" ++DFORTRANMP="$FCOMP -c -O1 -qno-offload -implicitnone -stand f18 -standard-semantics -assume nostd_mod_proc_name -safe-cray-ptr $PROFILE -zero -mp1 -WB $I8FFLAGS -I$MARC_SOURCE/common \ ++ -fpp -ftz -diag-disable 5268 -warn declarations -warn general -warn usage -warn interfaces -warn ignore_loc -warn alignments -DMARC4DAMASK=2023.2 -DDAMASKVERSION=$DAMASKVERSION \ ++ -qopenmp -qopenmp-threadprivate=compat\ ++ $MUMPS_INCLUDE $I8DEFINES -DLinux -DLINUX -DLinux_intel $FDEFINES $DDM $SOLVERFLAGS -I$KDTREE2_MOD -I$MARC_MOD" ++DFORTHIGHMP="$FCOMP -c -O3 -qno-offload -implicitnone -stand f18 -standard-semantics -assume nostd_mod_proc_name -safe-cray-ptr $PROFILE -zero -mp1 -WB $I8FFLAGS -I$MARC_SOURCE/common \ ++ -fpp -ftz -diag-disable 5268 -warn declarations -warn general -warn usage -warn interfaces -warn ignore_loc -warn alignments -DMARC4DAMASK=2023.2 -DDAMASKVERSION=$DAMASKVERSION \ ++ -qopenmp -qopenmp-threadprivate=compat\ ++ $MUMPS_INCLUDE $I8DEFINES -DLinux -DLINUX -DLinux_intel $FDEFINES $DDM $SOLVERFLAGS -I$KDTREE2_MOD -I$MARC_MOD" ++ + if test "$MARCDEBUG" = "ON" + then + FORTLOW="$FCOMP $FORT_OPT $PROFILE $I8FFLAGS -I$MARC_SOURCE/common \ +@@ -783,7 +815,7 @@ + + SOLVERLIBS="${BCSSOLVERLIBS} ${VKISOLVERLIBS} ${CASISOLVERLIBS} ${MF2SOLVERLIBS} \ + -L$MARC_MKL \ +- $MARC_LIB/blas_src.a ${ACSI_LIB}/ACSI_MarcLib.a $KDTREE2_LIB/libkdtree2.a $MARC_LIB/libtetmeshinterface.a $MARC_LIB/libcaefatigueinterface.a -L$MARC_LIB -lmkl_blacs_intelmpi_ilp64 -lmkl_scalapack_ilp64 -lmkl_intel_ilp64 -lmkl_intel_thread -lmkl_core -liomp5 -ltetmesh -ltetadapt -lmeshgems -lmg-tetra -lmeshgems_stubs -lCATMshMesherCore -lCATMshCVMforCSM -lmg-hybrid -lmg-cadsurf -lmg-hexa $HDF_LIBS $SOLVER2LIBS $RTREE_LIB/librtree_lib.a $SFGEO_LIB/libgeo_lib.a" ++ $MARC_LIB/blas_src.a ${ACSI_LIB}/ACSI_MarcLib.a $KDTREE2_LIB/libkdtree2.a $MARC_LIB/libtetmeshinterface.a $MARC_LIB/libcaefatigueinterface.a -L$MARC_LIB -lmkl_blacs_intelmpi_ilp64 -lmkl_scalapack_ilp64 -lmkl_intel_ilp64 -lmkl_intel_thread -lmkl_core -liomp5 -ltetmesh -ltetadapt -lmeshgems -lmg-tetra -lmeshgems_stubs -lCATMshMesherCore -lCATMshCVMforCSM -lmg-hybrid -lmg-cadsurf -lmg-hexa $HDF5_LIB $SOLVER2LIBS $RTREE_LIB/librtree_lib.a $SFGEO_LIB/libgeo_lib.a" + + SOLVERLIBS_DLL=${SOLVERLIBS} + if test "$AEM_DLL" -eq 1 diff --git a/install/MarcMentat/2023.2/Marc_tools/run_damask_hmp.patch b/install/MarcMentat/2023.2/Marc_tools/run_damask_hmp.patch new file mode 100644 index 000000000..aa632b3c2 --- /dev/null +++ b/install/MarcMentat/2023.2/Marc_tools/run_damask_hmp.patch @@ -0,0 +1,517 @@ +--- ++++ +@@ -136,6 +136,11 @@ + # is created. For job running in the background, the log # + # file is always created. Default is "yes" # + ############################################################################## ++# remove all Mentat paths from LD_LIBRARY_PATH ++LD_LIBRARY_PATH=:$LD_LIBRARY_PATH: ++LD_LIBRARY_PATH=${LD_LIBRARY_PATH//+([!(:)])mentat2023.2+([!(:)])/:} ++LD_LIBRARY_PATH=${LD_LIBRARY_PATH//+([(:)])/:} ++LD_LIBRARY_PATH=${LD_LIBRARY_PATH#:}; LD_LIBRARY_PATH=${LD_LIBRARY_PATH%:} + # set DIR to the directory in which this script is + REALCOM="`/bin/ls -l $0 |awk '{ print $NF; }'`" + DIR=`dirname $REALCOM` +@@ -302,7 +307,23 @@ + + . "$DIR/getarch" + ++ ++# getting user subroutine file name ++found=0 ++for i in "$@"; do ++ if test $found = 1; then ++ DAMASK_USER=$i ++ found=0 ++ fi ++ case $i in ++ -u* | -U*) ++ found=1 ++ ;; ++ esac ++done ++# sourcing include_linux64 (needs DAMASK_USER to be set) + . $MARC_INCLUDE ++ + # + + # +@@ -405,7 +426,7 @@ + did= + vid= + user= +-usersubname= ++usernoext= + objs= + qid=background + cpu= +@@ -676,50 +697,19 @@ + esac + ;; + -u* | -U*) +- user=`dirname $value`/`$BASENAME $value .f` +- usersubname=$user +- basefile=`$BASENAME $value` +- if test ${basefile##*.} = f +- then +- user=`dirname $value`/`$BASENAME $value .f` +- usersubname=$user.f +- elif test ${basefile##*.} = F +- then +- user=`dirname $value`/`$BASENAME $value .F` +- usersubname=$user.F +- elif test ${basefile##*.} = f90 +- then +- user=`dirname $value`/`$BASENAME $value .f90` +- usersubname=$user.f90 +- elif test ${basefile##*.} = F90 +- then +- user=`dirname $value`/`$BASENAME $value .F90` +- usersubname=$user.F90 +- fi ++ user=$value + case $user in + \/*) + ;; + *) + user=`pwd`/$user +- usersubname=`pwd`/$usersubname + ;; + esac +- if test ! -f $usersubname +- then +- if test -f $usersubname.f +- then +- usersubname=$usersubname.f +- elif test -f $usersubname.F +- then +- usersubname=$usersubname.F +- elif test -f $usersubname.f90 +- then +- usersubname=$usersubname.f90 +- elif test -f $usersubname.F90 +- then +- usersubname=$usersubname.F90 +- fi +- fi ++ usernoext=$user ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .F` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .for` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f90` + ;; + -obj | -OBJ) + objs="$value" +@@ -1207,12 +1197,12 @@ + fi + fi + fi +- if test "$usersubname" ++ if test "$user" + then +- if test ! -f $usersubname ++ if test ! -f $user + then + error="$error +-user subroutine file $usersubname not accessible" ++user subroutine file $user not accessible" + fi + fi + if test "$objs" +@@ -1531,7 +1521,7 @@ + Marc shared lib : $progdll + Version type : $mode + Job ID : $DIRJID/$jid$extra_job_info +-User subroutine name : $usersubname ++User subroutine name : $user + User objects/libs : $objs + Restart file job ID : $rid + Substructure file ID : $sid +@@ -1564,7 +1554,7 @@ + Marc shared lib : $progdll + Version type : $mode + Job ID : $DIRJID/$jid$extra_job_info +-User subroutine name : $usersubname ++User subroutine name : $user + User objects/libs : $objs + Restart file job ID : $rid + Substructure file ID : $sid +@@ -1687,7 +1677,7 @@ + ;; + esac + fi +- $ECHO "User subroutine name ($usersubname)? $ECHOTXT" ++ $ECHO "User subroutine name ($user)? $ECHOTXT" + read value + if test "$value" + then +@@ -1696,50 +1686,19 @@ + user= + ;; + *) +- user=`dirname $value`/`$BASENAME $value .f` +- usersubname=$user +- basefile=`$BASENAME $value` +- if test ${basefile##*.} = f +- then +- user=`dirname $value`/`$BASENAME $value .f` +- usersubname=$user.f +- elif test ${basefile##*.} = F +- then +- user=`dirname $value`/`$BASENAME $value .F` +- usersubname=$user.F +- elif test ${basefile##*.} = f90 +- then +- user=`dirname $value`/`$BASENAME $value .f90` +- usersubname=$user.f90 +- elif test ${basefile##*.} = F90 +- then +- user=`dirname $value`/`$BASENAME $value .F90` +- usersubname=$user.F90 +- fi ++ user=$value + case $user in + \/*) + ;; + *) + user=`pwd`/$user +- usersubname=`pwd`/$usersubname + ;; + esac +- if test ! -f $usersubname +- then +- if test -f $usersubname.f +- then +- usersubname=$usersubname.f +- elif test -f $usersubname.F +- then +- usersubname=$usersubname.F +- elif test -f $usersubname.f90 +- then +- usersubname=$usersubname.f90 +- elif test -f $usersubname.F90 +- then +- usersubname=$usersubname.F90 +- fi +- fi ++ usernoext=$user ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .F` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .for` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f90` + ;; + esac + fi +@@ -2274,11 +2233,12 @@ + # + # user subroutine used + # ++# add DAMASK options for linking ++ DAMASK="-lstdc++" + + if test "$user" + then +-# program=$user.marc +- program=$DIRJOB/`$BASENAME $user .f`.marc ++ program=$usernoext.marc + case $program in + \/* | \.\/*) + bd= +@@ -2391,7 +2351,7 @@ + fi + if test "$user" + then +- execpath=$DIRJOB/`$BASENAME $user .f`.marc ++ execpath=$usernoext.marc + usersub=1 + fi + export execpath +@@ -3274,44 +3234,27 @@ + echo + if test "$user" + then +- userobj=$DIRJOB/`$BASENAME $user .f`.o +- basefile=`$BASENAME $usersubname` +- if test ${basefile##*.} = f +- then +- usersub=$DIRJOB/`$BASENAME $user .f`.F +- ln -sf "$user.f" "$usersub" +- else +- usersub=$usersubname +- fi +- ++ userobj=$usernoext.o + fi + cat > $jid.runmarcscript << END4 + if test "$user" + then +- if test ${basefile##*.} = f +- then +- ln -sf "$user.f" "$usersub" +- fi + if test $MACHINENAME = "CRAY" + then +- $FORTRAN $usersub || \ ++ $DFORTHIGHMP $user || \ + { +- echo "$0: compile failed for $user.f" ++ echo "$0: compile failed for $user" + exit 1 + } + /bin/rm $program 2>/dev/null + else +- $FORTRAN $usersub -o $userobj || \ ++ $DFORTHIGHMP $user -o $userobj || \ + { +- echo "$0: compile failed for $user.f" ++ echo "$0: compile failed for $user" + exit 1 + } + /bin/rm $program 2>/dev/null + fi +- if test ${basefile##*.} = f +- then +- /bin/rm -f "$usersub" +- fi + fi + + +@@ -3332,6 +3275,7 @@ + $TKLIBS \ + $MRCLIBS \ + $METISLIBS \ ++ $DAMASK \ + $SFLIB \ + $OPENSSL_LIB \ + $SYSLIBS \ +@@ -3345,6 +3289,9 @@ + prgsav=yes + fi + /bin/rm $userobj 2>/dev/null ++/bin/rm $DIRJOB/*.mod 2>/dev/null ++/bin/rm $DIRJOB/*.smod 2>/dev/null ++/bin/rm $DIRJOB/*_genmod.f90 2>/dev/null + + # + # run marc +@@ -3391,7 +3338,7 @@ + fi + else + if test $cpdll = yes; then +- filename=`basename $usersubname .f` ++ filename=$usernoext + /bin/cp $DIRJOB/$marcdll $DIRJOB/${filename}_$marcdll 2>/dev/null + fi + if test $rmdll = yes +@@ -3557,7 +3504,7 @@ + # first copy over the user sub if local directories + if test ${dirstatus[$counter]} = "local" + then +- $RCP $user.f $i:$DIR1/ ++ $RCP $user $i:$DIR1/ + fi + # do the compilation on the other machine + if test ${dirstatus[$counter]} = "shared" +@@ -3570,21 +3517,21 @@ + remoteuser=$DIR1/`$BASENAME $user` + $RSH $i /bin/rm $remoteprog 2> /dev/null + echo +- $RSH $i $DIR2/tools/comp_user $DIR2 $DIR1 $remoteuser $remoteprog ++ $RSH $i $DIR2/tools/comp_damask_hmp $DIR2 $DIR1 $remoteuser $remoteprog + # check if successful, the new executable should be there + line=`$RSH $i /bin/ls $remoteprog 2> /dev/null` + if test "$line" + then + echo compilation and linking successful on host $i + else +- echo "$0: compile failed for $user.f on host $i" ++ echo "$0: compile failed for $user on host $i" + echo " $PRODUCT Exit number 3" + exit 1 + fi + # remove the user subroutine on remote machine + if test ${dirstatus[$counter]} = "local" + then +- $RSH $i /bin/rm $remoteuser.f 2> /dev/null ++ $RSH $i /bin/rm $remoteuser 2> /dev/null + fi + fi + fi +@@ -3594,39 +3541,27 @@ + if test "$userhost" + then + echo +- echo "Compiling and linking user subroutine $user.f on host `hostname`" +- fi +- userobj=$DIRJOB/`$BASENAME $user .f`.o +- basefile=`$BASENAME $usersubname` +- if test ${basefile##*.} = f +- then +- usersub=$DIRJOB/`$BASENAME $user .f`.F +- ln -sf "$user.f" "$usersub" +- else +- usersub=$usersubname ++ echo "Compiling and linking user subroutine $user on host `hostname`" + fi ++ userobj=$usernoext.o + if test $MACHINENAME = "CRAY" + then +- $FORTRAN $usersub || \ ++ $DFORTHIGHMP $user || \ + { +- echo "$0: compile failed for $user.f" ++ echo "$0: compile failed for $user" + echo " $PRODUCT Exit number 3" + exit 1 + } + /bin/rm $program 2>/dev/null + else +- $FORTRAN $usersub -o $userobj || \ ++ $DFORTHIGHMP $user -o $userobj || \ + { +- echo "$0: compile failed for $user.f" ++ echo "$0: compile failed for $user" + echo " $PRODUCT Exit number 3" + exit 1 + } + /bin/rm $program 2>/dev/null + fi +- if test ${basefile##*.} = f +- then +- /bin/rm -f "$usersub" +- fi + fi # if test $user + + +@@ -3647,6 +3582,7 @@ + $TKLIBS \ + $MRCLIBS \ + $METISLIBS \ ++ $DAMASK \ + $SFLIB \ + $OPENSSL_LIB \ + $SYSLIBS \ +@@ -3688,6 +3624,9 @@ + prgsav=yes + fi # if test $link + /bin/rm $userobj 2>/dev/null ++/bin/rm $DIRJOB/*.mod 2>/dev/null ++/bin/rm $DIRJOB/*.smod 2>/dev/null ++/bin/rm $DIRJOB/*_genmod.f90 2>/dev/null + + # + # run marc +@@ -3781,7 +3720,7 @@ + else + #dllrun >0 + if test $cpdll = yes; then +- filename=`basename $usersubname .f` ++ filename=$usernoext + /bin/cp $DIRJOB/$marcdll $DIRJOB/${filename}_$marcdll 2>/dev/null + fi + if test $rmdll = yes;then +@@ -3906,7 +3845,7 @@ + # first copy over the user sub if local directories + if test ${dirstatus[$counter]} = "local" + then +- $RCP $user.f $i:$DIR1/ ++ $RCP $user $i:$DIR1/ + fi + # do the compilation on the other machine + if test ${dirstatus[$counter]} = "shared" +@@ -3919,20 +3858,20 @@ + remoteuser=$DIR1/`$BASENAME $user` + $RSH $i /bin/rm $remoteprog 2> /dev/null + echo +- $RSH $i $DIR2/tools/comp_user $DIR2 $DIR1 $remoteuser $remoteprog ++ $RSH $i $DIR2/tools/comp_damask_hmp $DIR2 $DIR1 $remoteuser $remoteprog + # check if successful, the new executable should be there + line=`$RSH $i /bin/ls $remoteprog 2> /dev/null` + if test "$line" + then + echo compilation and linking successful on host $i + else +- echo "$0: compile failed for $user.f on host $i" ++ echo "$0: compile failed for $user on host $i" + exit 1 + fi + # remove the user subroutine on remote machine + if test ${dirstatus[$counter]} = "local" + then +- $RSH $i /bin/rm $remoteuser.f 2> /dev/null ++ $RSH $i /bin/rm $remoteuser 2> /dev/null + fi + fi + fi +@@ -3942,37 +3881,25 @@ + if test "$userhost" + then + echo +- echo "Compiling and linking user subroutine $user.f on host `hostname`" +- fi +- userobj=$DIRJOB/`$BASENAME $user .f`.o +- basefile=`$BASENAME $usersubname` +- if test ${basefile##*.} = f +- then +- usersub=$DIRJOB/`$BASENAME $user .f`.F +- ln -sf "$user.f" "$usersub" +- else +- usersub=$usersubname ++ echo "Compiling and linking user subroutine $user on host `hostname`" + fi ++ userobj=$usernoext.o + if test $MACHINENAME = "CRAY" + then +- $FORTRAN $usersub || \ ++ $DFORTHIGHMP $user || \ + { +- echo "$0: compile failed for $user.f" ++ echo "$0: compile failed for $user" + exit 1 + } + /bin/rm $program 2>/dev/null + else +- $FORTRAN $usersub -o $userobj || \ ++ $DFORTHIGHMP $user -o $userobj || \ + { +- echo "$0: compile failed for $user.f" ++ echo "$0: compile failed for $user" + exit 1 + } + /bin/rm $program 2>/dev/null + fi +- if test ${basefile##*.} = f +- then +- /bin/rm -f "$usersub" +- fi + fi # if test $user + + +@@ -3993,6 +3920,7 @@ + $TKLIBS \ + $MRCLIBS \ + $METISLIBS \ ++ $DAMASK \ + $SFLIB \ + $OPENSSL_LIB \ + $SYSLIBS \ +@@ -4033,7 +3961,9 @@ + prgsav=yes + fi # if test $link + /bin/rm $userobj 2>/dev/null +- ++/bin/rm $DIRJOB/*.mod 2>/dev/null ++/bin/rm $DIRJOB/*.smod 2>/dev/null ++/bin/rm $DIRJOB/*_genmod.f90 2>/dev/null + # done if no job id given + if test -z "$jid" + then +@@ -4152,7 +4082,7 @@ + else + #dllrun >0 + if test $cpdll = yes; then +- filename=`basename $usersubname .f` ++ filename=$usernoext + /bin/cp $DIRJOB/$marcdll $DIRJOB/${filename}_$marcdll 2>/dev/null + fi + if test $rmdll = yes;then diff --git a/install/MarcMentat/2023.2/Marc_tools/run_damask_lmp.patch b/install/MarcMentat/2023.2/Marc_tools/run_damask_lmp.patch new file mode 100644 index 000000000..92d30eb56 --- /dev/null +++ b/install/MarcMentat/2023.2/Marc_tools/run_damask_lmp.patch @@ -0,0 +1,517 @@ +--- ++++ +@@ -136,6 +136,11 @@ + # is created. For job running in the background, the log # + # file is always created. Default is "yes" # + ############################################################################## ++# remove all Mentat paths from LD_LIBRARY_PATH ++LD_LIBRARY_PATH=:$LD_LIBRARY_PATH: ++LD_LIBRARY_PATH=${LD_LIBRARY_PATH//+([!(:)])mentat2023.2+([!(:)])/:} ++LD_LIBRARY_PATH=${LD_LIBRARY_PATH//+([(:)])/:} ++LD_LIBRARY_PATH=${LD_LIBRARY_PATH#:}; LD_LIBRARY_PATH=${LD_LIBRARY_PATH%:} + # set DIR to the directory in which this script is + REALCOM="`/bin/ls -l $0 |awk '{ print $NF; }'`" + DIR=`dirname $REALCOM` +@@ -302,7 +307,23 @@ + + . "$DIR/getarch" + ++ ++# getting user subroutine file name ++found=0 ++for i in "$@"; do ++ if test $found = 1; then ++ DAMASK_USER=$i ++ found=0 ++ fi ++ case $i in ++ -u* | -U*) ++ found=1 ++ ;; ++ esac ++done ++# sourcing include_linux64 (needs DAMASK_USER to be set) + . $MARC_INCLUDE ++ + # + + # +@@ -405,7 +426,7 @@ + did= + vid= + user= +-usersubname= ++usernoext= + objs= + qid=background + cpu= +@@ -676,50 +697,19 @@ + esac + ;; + -u* | -U*) +- user=`dirname $value`/`$BASENAME $value .f` +- usersubname=$user +- basefile=`$BASENAME $value` +- if test ${basefile##*.} = f +- then +- user=`dirname $value`/`$BASENAME $value .f` +- usersubname=$user.f +- elif test ${basefile##*.} = F +- then +- user=`dirname $value`/`$BASENAME $value .F` +- usersubname=$user.F +- elif test ${basefile##*.} = f90 +- then +- user=`dirname $value`/`$BASENAME $value .f90` +- usersubname=$user.f90 +- elif test ${basefile##*.} = F90 +- then +- user=`dirname $value`/`$BASENAME $value .F90` +- usersubname=$user.F90 +- fi ++ user=$value + case $user in + \/*) + ;; + *) + user=`pwd`/$user +- usersubname=`pwd`/$usersubname + ;; + esac +- if test ! -f $usersubname +- then +- if test -f $usersubname.f +- then +- usersubname=$usersubname.f +- elif test -f $usersubname.F +- then +- usersubname=$usersubname.F +- elif test -f $usersubname.f90 +- then +- usersubname=$usersubname.f90 +- elif test -f $usersubname.F90 +- then +- usersubname=$usersubname.F90 +- fi +- fi ++ usernoext=$user ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .F` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .for` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f90` + ;; + -obj | -OBJ) + objs="$value" +@@ -1207,12 +1197,12 @@ + fi + fi + fi +- if test "$usersubname" ++ if test "$user" + then +- if test ! -f $usersubname ++ if test ! -f $user + then + error="$error +-user subroutine file $usersubname not accessible" ++user subroutine file $user not accessible" + fi + fi + if test "$objs" +@@ -1531,7 +1521,7 @@ + Marc shared lib : $progdll + Version type : $mode + Job ID : $DIRJID/$jid$extra_job_info +-User subroutine name : $usersubname ++User subroutine name : $user + User objects/libs : $objs + Restart file job ID : $rid + Substructure file ID : $sid +@@ -1564,7 +1554,7 @@ + Marc shared lib : $progdll + Version type : $mode + Job ID : $DIRJID/$jid$extra_job_info +-User subroutine name : $usersubname ++User subroutine name : $user + User objects/libs : $objs + Restart file job ID : $rid + Substructure file ID : $sid +@@ -1687,7 +1677,7 @@ + ;; + esac + fi +- $ECHO "User subroutine name ($usersubname)? $ECHOTXT" ++ $ECHO "User subroutine name ($user)? $ECHOTXT" + read value + if test "$value" + then +@@ -1696,50 +1686,19 @@ + user= + ;; + *) +- user=`dirname $value`/`$BASENAME $value .f` +- usersubname=$user +- basefile=`$BASENAME $value` +- if test ${basefile##*.} = f +- then +- user=`dirname $value`/`$BASENAME $value .f` +- usersubname=$user.f +- elif test ${basefile##*.} = F +- then +- user=`dirname $value`/`$BASENAME $value .F` +- usersubname=$user.F +- elif test ${basefile##*.} = f90 +- then +- user=`dirname $value`/`$BASENAME $value .f90` +- usersubname=$user.f90 +- elif test ${basefile##*.} = F90 +- then +- user=`dirname $value`/`$BASENAME $value .F90` +- usersubname=$user.F90 +- fi ++ user=$value + case $user in + \/*) + ;; + *) + user=`pwd`/$user +- usersubname=`pwd`/$usersubname + ;; + esac +- if test ! -f $usersubname +- then +- if test -f $usersubname.f +- then +- usersubname=$usersubname.f +- elif test -f $usersubname.F +- then +- usersubname=$usersubname.F +- elif test -f $usersubname.f90 +- then +- usersubname=$usersubname.f90 +- elif test -f $usersubname.F90 +- then +- usersubname=$usersubname.F90 +- fi +- fi ++ usernoext=$user ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .F` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .for` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f90` + ;; + esac + fi +@@ -2274,11 +2233,12 @@ + # + # user subroutine used + # ++# add DAMASK options for linking ++ DAMASK="-lstdc++" + + if test "$user" + then +-# program=$user.marc +- program=$DIRJOB/`$BASENAME $user .f`.marc ++ program=$usernoext.marc + case $program in + \/* | \.\/*) + bd= +@@ -2391,7 +2351,7 @@ + fi + if test "$user" + then +- execpath=$DIRJOB/`$BASENAME $user .f`.marc ++ execpath=$usernoext.marc + usersub=1 + fi + export execpath +@@ -3274,44 +3234,27 @@ + echo + if test "$user" + then +- userobj=$DIRJOB/`$BASENAME $user .f`.o +- basefile=`$BASENAME $usersubname` +- if test ${basefile##*.} = f +- then +- usersub=$DIRJOB/`$BASENAME $user .f`.F +- ln -sf "$user.f" "$usersub" +- else +- usersub=$usersubname +- fi +- ++ userobj=$usernoext.o + fi + cat > $jid.runmarcscript << END4 + if test "$user" + then +- if test ${basefile##*.} = f +- then +- ln -sf "$user.f" "$usersub" +- fi + if test $MACHINENAME = "CRAY" + then +- $FORTRAN $usersub || \ ++ $DFORTLOWMP $user || \ + { +- echo "$0: compile failed for $user.f" ++ echo "$0: compile failed for $user" + exit 1 + } + /bin/rm $program 2>/dev/null + else +- $FORTRAN $usersub -o $userobj || \ ++ $DFORTLOWMP $user -o $userobj || \ + { +- echo "$0: compile failed for $user.f" ++ echo "$0: compile failed for $user" + exit 1 + } + /bin/rm $program 2>/dev/null + fi +- if test ${basefile##*.} = f +- then +- /bin/rm -f "$usersub" +- fi + fi + + +@@ -3332,6 +3275,7 @@ + $TKLIBS \ + $MRCLIBS \ + $METISLIBS \ ++ $DAMASK \ + $SFLIB \ + $OPENSSL_LIB \ + $SYSLIBS \ +@@ -3345,6 +3289,9 @@ + prgsav=yes + fi + /bin/rm $userobj 2>/dev/null ++/bin/rm $DIRJOB/*.mod 2>/dev/null ++/bin/rm $DIRJOB/*.smod 2>/dev/null ++/bin/rm $DIRJOB/*_genmod.f90 2>/dev/null + + # + # run marc +@@ -3391,7 +3338,7 @@ + fi + else + if test $cpdll = yes; then +- filename=`basename $usersubname .f` ++ filename=$usernoext + /bin/cp $DIRJOB/$marcdll $DIRJOB/${filename}_$marcdll 2>/dev/null + fi + if test $rmdll = yes +@@ -3557,7 +3504,7 @@ + # first copy over the user sub if local directories + if test ${dirstatus[$counter]} = "local" + then +- $RCP $user.f $i:$DIR1/ ++ $RCP $user $i:$DIR1/ + fi + # do the compilation on the other machine + if test ${dirstatus[$counter]} = "shared" +@@ -3570,21 +3517,21 @@ + remoteuser=$DIR1/`$BASENAME $user` + $RSH $i /bin/rm $remoteprog 2> /dev/null + echo +- $RSH $i $DIR2/tools/comp_user $DIR2 $DIR1 $remoteuser $remoteprog ++ $RSH $i $DIR2/tools/comp_damask_lmp $DIR2 $DIR1 $remoteuser $remoteprog + # check if successful, the new executable should be there + line=`$RSH $i /bin/ls $remoteprog 2> /dev/null` + if test "$line" + then + echo compilation and linking successful on host $i + else +- echo "$0: compile failed for $user.f on host $i" ++ echo "$0: compile failed for $user on host $i" + echo " $PRODUCT Exit number 3" + exit 1 + fi + # remove the user subroutine on remote machine + if test ${dirstatus[$counter]} = "local" + then +- $RSH $i /bin/rm $remoteuser.f 2> /dev/null ++ $RSH $i /bin/rm $remoteuser 2> /dev/null + fi + fi + fi +@@ -3594,39 +3541,27 @@ + if test "$userhost" + then + echo +- echo "Compiling and linking user subroutine $user.f on host `hostname`" +- fi +- userobj=$DIRJOB/`$BASENAME $user .f`.o +- basefile=`$BASENAME $usersubname` +- if test ${basefile##*.} = f +- then +- usersub=$DIRJOB/`$BASENAME $user .f`.F +- ln -sf "$user.f" "$usersub" +- else +- usersub=$usersubname ++ echo "Compiling and linking user subroutine $user on host `hostname`" + fi ++ userobj=$usernoext.o + if test $MACHINENAME = "CRAY" + then +- $FORTRAN $usersub || \ ++ $DFORTLOWMP $user || \ + { +- echo "$0: compile failed for $user.f" ++ echo "$0: compile failed for $user" + echo " $PRODUCT Exit number 3" + exit 1 + } + /bin/rm $program 2>/dev/null + else +- $FORTRAN $usersub -o $userobj || \ ++ $DFORTLOWMP $user -o $userobj || \ + { +- echo "$0: compile failed for $user.f" ++ echo "$0: compile failed for $user" + echo " $PRODUCT Exit number 3" + exit 1 + } + /bin/rm $program 2>/dev/null + fi +- if test ${basefile##*.} = f +- then +- /bin/rm -f "$usersub" +- fi + fi # if test $user + + +@@ -3647,6 +3582,7 @@ + $TKLIBS \ + $MRCLIBS \ + $METISLIBS \ ++ $DAMASK \ + $SFLIB \ + $OPENSSL_LIB \ + $SYSLIBS \ +@@ -3688,6 +3624,9 @@ + prgsav=yes + fi # if test $link + /bin/rm $userobj 2>/dev/null ++/bin/rm $DIRJOB/*.mod 2>/dev/null ++/bin/rm $DIRJOB/*.smod 2>/dev/null ++/bin/rm $DIRJOB/*_genmod.f90 2>/dev/null + + # + # run marc +@@ -3781,7 +3720,7 @@ + else + #dllrun >0 + if test $cpdll = yes; then +- filename=`basename $usersubname .f` ++ filename=$usernoext + /bin/cp $DIRJOB/$marcdll $DIRJOB/${filename}_$marcdll 2>/dev/null + fi + if test $rmdll = yes;then +@@ -3906,7 +3845,7 @@ + # first copy over the user sub if local directories + if test ${dirstatus[$counter]} = "local" + then +- $RCP $user.f $i:$DIR1/ ++ $RCP $user $i:$DIR1/ + fi + # do the compilation on the other machine + if test ${dirstatus[$counter]} = "shared" +@@ -3919,20 +3858,20 @@ + remoteuser=$DIR1/`$BASENAME $user` + $RSH $i /bin/rm $remoteprog 2> /dev/null + echo +- $RSH $i $DIR2/tools/comp_user $DIR2 $DIR1 $remoteuser $remoteprog ++ $RSH $i $DIR2/tools/comp_damask_lmp $DIR2 $DIR1 $remoteuser $remoteprog + # check if successful, the new executable should be there + line=`$RSH $i /bin/ls $remoteprog 2> /dev/null` + if test "$line" + then + echo compilation and linking successful on host $i + else +- echo "$0: compile failed for $user.f on host $i" ++ echo "$0: compile failed for $user on host $i" + exit 1 + fi + # remove the user subroutine on remote machine + if test ${dirstatus[$counter]} = "local" + then +- $RSH $i /bin/rm $remoteuser.f 2> /dev/null ++ $RSH $i /bin/rm $remoteuser 2> /dev/null + fi + fi + fi +@@ -3942,37 +3881,25 @@ + if test "$userhost" + then + echo +- echo "Compiling and linking user subroutine $user.f on host `hostname`" +- fi +- userobj=$DIRJOB/`$BASENAME $user .f`.o +- basefile=`$BASENAME $usersubname` +- if test ${basefile##*.} = f +- then +- usersub=$DIRJOB/`$BASENAME $user .f`.F +- ln -sf "$user.f" "$usersub" +- else +- usersub=$usersubname ++ echo "Compiling and linking user subroutine $user on host `hostname`" + fi ++ userobj=$usernoext.o + if test $MACHINENAME = "CRAY" + then +- $FORTRAN $usersub || \ ++ $DFORTLOWMP $user || \ + { +- echo "$0: compile failed for $user.f" ++ echo "$0: compile failed for $user" + exit 1 + } + /bin/rm $program 2>/dev/null + else +- $FORTRAN $usersub -o $userobj || \ ++ $DFORTLOWMP $user -o $userobj || \ + { +- echo "$0: compile failed for $user.f" ++ echo "$0: compile failed for $user" + exit 1 + } + /bin/rm $program 2>/dev/null + fi +- if test ${basefile##*.} = f +- then +- /bin/rm -f "$usersub" +- fi + fi # if test $user + + +@@ -3993,6 +3920,7 @@ + $TKLIBS \ + $MRCLIBS \ + $METISLIBS \ ++ $DAMASK \ + $SFLIB \ + $OPENSSL_LIB \ + $SYSLIBS \ +@@ -4033,7 +3961,9 @@ + prgsav=yes + fi # if test $link + /bin/rm $userobj 2>/dev/null +- ++/bin/rm $DIRJOB/*.mod 2>/dev/null ++/bin/rm $DIRJOB/*.smod 2>/dev/null ++/bin/rm $DIRJOB/*_genmod.f90 2>/dev/null + # done if no job id given + if test -z "$jid" + then +@@ -4152,7 +4082,7 @@ + else + #dllrun >0 + if test $cpdll = yes; then +- filename=`basename $usersubname .f` ++ filename=$usernoext + /bin/cp $DIRJOB/$marcdll $DIRJOB/${filename}_$marcdll 2>/dev/null + fi + if test $rmdll = yes;then diff --git a/install/MarcMentat/2023.2/Marc_tools/run_damask_mp.patch b/install/MarcMentat/2023.2/Marc_tools/run_damask_mp.patch new file mode 100644 index 000000000..d43924bd4 --- /dev/null +++ b/install/MarcMentat/2023.2/Marc_tools/run_damask_mp.patch @@ -0,0 +1,517 @@ +--- ++++ +@@ -136,6 +136,11 @@ + # is created. For job running in the background, the log # + # file is always created. Default is "yes" # + ############################################################################## ++# remove all Mentat paths from LD_LIBRARY_PATH ++LD_LIBRARY_PATH=:$LD_LIBRARY_PATH: ++LD_LIBRARY_PATH=${LD_LIBRARY_PATH//+([!(:)])mentat2023.2+([!(:)])/:} ++LD_LIBRARY_PATH=${LD_LIBRARY_PATH//+([(:)])/:} ++LD_LIBRARY_PATH=${LD_LIBRARY_PATH#:}; LD_LIBRARY_PATH=${LD_LIBRARY_PATH%:} + # set DIR to the directory in which this script is + REALCOM="`/bin/ls -l $0 |awk '{ print $NF; }'`" + DIR=`dirname $REALCOM` +@@ -302,7 +307,23 @@ + + . "$DIR/getarch" + ++ ++# getting user subroutine file name ++found=0 ++for i in "$@"; do ++ if test $found = 1; then ++ DAMASK_USER=$i ++ found=0 ++ fi ++ case $i in ++ -u* | -U*) ++ found=1 ++ ;; ++ esac ++done ++# sourcing include_linux64 (needs DAMASK_USER to be set) + . $MARC_INCLUDE ++ + # + + # +@@ -405,7 +426,7 @@ + did= + vid= + user= +-usersubname= ++usernoext= + objs= + qid=background + cpu= +@@ -676,50 +697,19 @@ + esac + ;; + -u* | -U*) +- user=`dirname $value`/`$BASENAME $value .f` +- usersubname=$user +- basefile=`$BASENAME $value` +- if test ${basefile##*.} = f +- then +- user=`dirname $value`/`$BASENAME $value .f` +- usersubname=$user.f +- elif test ${basefile##*.} = F +- then +- user=`dirname $value`/`$BASENAME $value .F` +- usersubname=$user.F +- elif test ${basefile##*.} = f90 +- then +- user=`dirname $value`/`$BASENAME $value .f90` +- usersubname=$user.f90 +- elif test ${basefile##*.} = F90 +- then +- user=`dirname $value`/`$BASENAME $value .F90` +- usersubname=$user.F90 +- fi ++ user=$value + case $user in + \/*) + ;; + *) + user=`pwd`/$user +- usersubname=`pwd`/$usersubname + ;; + esac +- if test ! -f $usersubname +- then +- if test -f $usersubname.f +- then +- usersubname=$usersubname.f +- elif test -f $usersubname.F +- then +- usersubname=$usersubname.F +- elif test -f $usersubname.f90 +- then +- usersubname=$usersubname.f90 +- elif test -f $usersubname.F90 +- then +- usersubname=$usersubname.F90 +- fi +- fi ++ usernoext=$user ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .F` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .for` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f90` + ;; + -obj | -OBJ) + objs="$value" +@@ -1207,12 +1197,12 @@ + fi + fi + fi +- if test "$usersubname" ++ if test "$user" + then +- if test ! -f $usersubname ++ if test ! -f $user + then + error="$error +-user subroutine file $usersubname not accessible" ++user subroutine file $user not accessible" + fi + fi + if test "$objs" +@@ -1531,7 +1521,7 @@ + Marc shared lib : $progdll + Version type : $mode + Job ID : $DIRJID/$jid$extra_job_info +-User subroutine name : $usersubname ++User subroutine name : $user + User objects/libs : $objs + Restart file job ID : $rid + Substructure file ID : $sid +@@ -1564,7 +1554,7 @@ + Marc shared lib : $progdll + Version type : $mode + Job ID : $DIRJID/$jid$extra_job_info +-User subroutine name : $usersubname ++User subroutine name : $user + User objects/libs : $objs + Restart file job ID : $rid + Substructure file ID : $sid +@@ -1687,7 +1677,7 @@ + ;; + esac + fi +- $ECHO "User subroutine name ($usersubname)? $ECHOTXT" ++ $ECHO "User subroutine name ($user)? $ECHOTXT" + read value + if test "$value" + then +@@ -1696,50 +1686,19 @@ + user= + ;; + *) +- user=`dirname $value`/`$BASENAME $value .f` +- usersubname=$user +- basefile=`$BASENAME $value` +- if test ${basefile##*.} = f +- then +- user=`dirname $value`/`$BASENAME $value .f` +- usersubname=$user.f +- elif test ${basefile##*.} = F +- then +- user=`dirname $value`/`$BASENAME $value .F` +- usersubname=$user.F +- elif test ${basefile##*.} = f90 +- then +- user=`dirname $value`/`$BASENAME $value .f90` +- usersubname=$user.f90 +- elif test ${basefile##*.} = F90 +- then +- user=`dirname $value`/`$BASENAME $value .F90` +- usersubname=$user.F90 +- fi ++ user=$value + case $user in + \/*) + ;; + *) + user=`pwd`/$user +- usersubname=`pwd`/$usersubname + ;; + esac +- if test ! -f $usersubname +- then +- if test -f $usersubname.f +- then +- usersubname=$usersubname.f +- elif test -f $usersubname.F +- then +- usersubname=$usersubname.F +- elif test -f $usersubname.f90 +- then +- usersubname=$usersubname.f90 +- elif test -f $usersubname.F90 +- then +- usersubname=$usersubname.F90 +- fi +- fi ++ usernoext=$user ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .F` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .for` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f90` + ;; + esac + fi +@@ -2274,11 +2233,12 @@ + # + # user subroutine used + # ++# add DAMASK options for linking ++ DAMASK="-lstdc++" + + if test "$user" + then +-# program=$user.marc +- program=$DIRJOB/`$BASENAME $user .f`.marc ++ program=$usernoext.marc + case $program in + \/* | \.\/*) + bd= +@@ -2391,7 +2351,7 @@ + fi + if test "$user" + then +- execpath=$DIRJOB/`$BASENAME $user .f`.marc ++ execpath=$usernoext.marc + usersub=1 + fi + export execpath +@@ -3274,44 +3234,27 @@ + echo + if test "$user" + then +- userobj=$DIRJOB/`$BASENAME $user .f`.o +- basefile=`$BASENAME $usersubname` +- if test ${basefile##*.} = f +- then +- usersub=$DIRJOB/`$BASENAME $user .f`.F +- ln -sf "$user.f" "$usersub" +- else +- usersub=$usersubname +- fi +- ++ userobj=$usernoext.o + fi + cat > $jid.runmarcscript << END4 + if test "$user" + then +- if test ${basefile##*.} = f +- then +- ln -sf "$user.f" "$usersub" +- fi + if test $MACHINENAME = "CRAY" + then +- $FORTRAN $usersub || \ ++ $DFORTRANMP $user || \ + { +- echo "$0: compile failed for $user.f" ++ echo "$0: compile failed for $user" + exit 1 + } + /bin/rm $program 2>/dev/null + else +- $FORTRAN $usersub -o $userobj || \ ++ $DFORTRANMP $user -o $userobj || \ + { +- echo "$0: compile failed for $user.f" ++ echo "$0: compile failed for $user" + exit 1 + } + /bin/rm $program 2>/dev/null + fi +- if test ${basefile##*.} = f +- then +- /bin/rm -f "$usersub" +- fi + fi + + +@@ -3332,6 +3275,7 @@ + $TKLIBS \ + $MRCLIBS \ + $METISLIBS \ ++ $DAMASK \ + $SFLIB \ + $OPENSSL_LIB \ + $SYSLIBS \ +@@ -3345,6 +3289,9 @@ + prgsav=yes + fi + /bin/rm $userobj 2>/dev/null ++/bin/rm $DIRJOB/*.mod 2>/dev/null ++/bin/rm $DIRJOB/*.smod 2>/dev/null ++/bin/rm $DIRJOB/*_genmod.f90 2>/dev/null + + # + # run marc +@@ -3391,7 +3338,7 @@ + fi + else + if test $cpdll = yes; then +- filename=`basename $usersubname .f` ++ filename=$usernoext + /bin/cp $DIRJOB/$marcdll $DIRJOB/${filename}_$marcdll 2>/dev/null + fi + if test $rmdll = yes +@@ -3557,7 +3504,7 @@ + # first copy over the user sub if local directories + if test ${dirstatus[$counter]} = "local" + then +- $RCP $user.f $i:$DIR1/ ++ $RCP $user $i:$DIR1/ + fi + # do the compilation on the other machine + if test ${dirstatus[$counter]} = "shared" +@@ -3570,21 +3517,21 @@ + remoteuser=$DIR1/`$BASENAME $user` + $RSH $i /bin/rm $remoteprog 2> /dev/null + echo +- $RSH $i $DIR2/tools/comp_user $DIR2 $DIR1 $remoteuser $remoteprog ++ $RSH $i $DIR2/tools/comp_damask_mp $DIR2 $DIR1 $remoteuser $remoteprog + # check if successful, the new executable should be there + line=`$RSH $i /bin/ls $remoteprog 2> /dev/null` + if test "$line" + then + echo compilation and linking successful on host $i + else +- echo "$0: compile failed for $user.f on host $i" ++ echo "$0: compile failed for $user on host $i" + echo " $PRODUCT Exit number 3" + exit 1 + fi + # remove the user subroutine on remote machine + if test ${dirstatus[$counter]} = "local" + then +- $RSH $i /bin/rm $remoteuser.f 2> /dev/null ++ $RSH $i /bin/rm $remoteuser 2> /dev/null + fi + fi + fi +@@ -3594,39 +3541,27 @@ + if test "$userhost" + then + echo +- echo "Compiling and linking user subroutine $user.f on host `hostname`" +- fi +- userobj=$DIRJOB/`$BASENAME $user .f`.o +- basefile=`$BASENAME $usersubname` +- if test ${basefile##*.} = f +- then +- usersub=$DIRJOB/`$BASENAME $user .f`.F +- ln -sf "$user.f" "$usersub" +- else +- usersub=$usersubname ++ echo "Compiling and linking user subroutine $user on host `hostname`" + fi ++ userobj=$usernoext.o + if test $MACHINENAME = "CRAY" + then +- $FORTRAN $usersub || \ ++ $DFORTRANMP $user || \ + { +- echo "$0: compile failed for $user.f" ++ echo "$0: compile failed for $user" + echo " $PRODUCT Exit number 3" + exit 1 + } + /bin/rm $program 2>/dev/null + else +- $FORTRAN $usersub -o $userobj || \ ++ $DFORTRANMP $user -o $userobj || \ + { +- echo "$0: compile failed for $user.f" ++ echo "$0: compile failed for $user" + echo " $PRODUCT Exit number 3" + exit 1 + } + /bin/rm $program 2>/dev/null + fi +- if test ${basefile##*.} = f +- then +- /bin/rm -f "$usersub" +- fi + fi # if test $user + + +@@ -3647,6 +3582,7 @@ + $TKLIBS \ + $MRCLIBS \ + $METISLIBS \ ++ $DAMASK \ + $SFLIB \ + $OPENSSL_LIB \ + $SYSLIBS \ +@@ -3688,6 +3624,9 @@ + prgsav=yes + fi # if test $link + /bin/rm $userobj 2>/dev/null ++/bin/rm $DIRJOB/*.mod 2>/dev/null ++/bin/rm $DIRJOB/*.smod 2>/dev/null ++/bin/rm $DIRJOB/*_genmod.f90 2>/dev/null + + # + # run marc +@@ -3781,7 +3720,7 @@ + else + #dllrun >0 + if test $cpdll = yes; then +- filename=`basename $usersubname .f` ++ filename=$usernoext + /bin/cp $DIRJOB/$marcdll $DIRJOB/${filename}_$marcdll 2>/dev/null + fi + if test $rmdll = yes;then +@@ -3906,7 +3845,7 @@ + # first copy over the user sub if local directories + if test ${dirstatus[$counter]} = "local" + then +- $RCP $user.f $i:$DIR1/ ++ $RCP $user $i:$DIR1/ + fi + # do the compilation on the other machine + if test ${dirstatus[$counter]} = "shared" +@@ -3919,20 +3858,20 @@ + remoteuser=$DIR1/`$BASENAME $user` + $RSH $i /bin/rm $remoteprog 2> /dev/null + echo +- $RSH $i $DIR2/tools/comp_user $DIR2 $DIR1 $remoteuser $remoteprog ++ $RSH $i $DIR2/tools/comp_damask_mp $DIR2 $DIR1 $remoteuser $remoteprog + # check if successful, the new executable should be there + line=`$RSH $i /bin/ls $remoteprog 2> /dev/null` + if test "$line" + then + echo compilation and linking successful on host $i + else +- echo "$0: compile failed for $user.f on host $i" ++ echo "$0: compile failed for $user on host $i" + exit 1 + fi + # remove the user subroutine on remote machine + if test ${dirstatus[$counter]} = "local" + then +- $RSH $i /bin/rm $remoteuser.f 2> /dev/null ++ $RSH $i /bin/rm $remoteuser 2> /dev/null + fi + fi + fi +@@ -3942,37 +3881,25 @@ + if test "$userhost" + then + echo +- echo "Compiling and linking user subroutine $user.f on host `hostname`" +- fi +- userobj=$DIRJOB/`$BASENAME $user .f`.o +- basefile=`$BASENAME $usersubname` +- if test ${basefile##*.} = f +- then +- usersub=$DIRJOB/`$BASENAME $user .f`.F +- ln -sf "$user.f" "$usersub" +- else +- usersub=$usersubname ++ echo "Compiling and linking user subroutine $user on host `hostname`" + fi ++ userobj=$usernoext.o + if test $MACHINENAME = "CRAY" + then +- $FORTRAN $usersub || \ ++ $DFORTRANMP $user || \ + { +- echo "$0: compile failed for $user.f" ++ echo "$0: compile failed for $user" + exit 1 + } + /bin/rm $program 2>/dev/null + else +- $FORTRAN $usersub -o $userobj || \ ++ $DFORTRANMP $user -o $userobj || \ + { +- echo "$0: compile failed for $user.f" ++ echo "$0: compile failed for $user" + exit 1 + } + /bin/rm $program 2>/dev/null + fi +- if test ${basefile##*.} = f +- then +- /bin/rm -f "$usersub" +- fi + fi # if test $user + + +@@ -3993,6 +3920,7 @@ + $TKLIBS \ + $MRCLIBS \ + $METISLIBS \ ++ $DAMASK \ + $SFLIB \ + $OPENSSL_LIB \ + $SYSLIBS \ +@@ -4033,7 +3961,9 @@ + prgsav=yes + fi # if test $link + /bin/rm $userobj 2>/dev/null +- ++/bin/rm $DIRJOB/*.mod 2>/dev/null ++/bin/rm $DIRJOB/*.smod 2>/dev/null ++/bin/rm $DIRJOB/*_genmod.f90 2>/dev/null + # done if no job id given + if test -z "$jid" + then +@@ -4152,7 +4082,7 @@ + else + #dllrun >0 + if test $cpdll = yes; then +- filename=`basename $usersubname .f` ++ filename=$usernoext + /bin/cp $DIRJOB/$marcdll $DIRJOB/${filename}_$marcdll 2>/dev/null + fi + if test $rmdll = yes;then diff --git a/install/MarcMentat/2023.2/Mentat_bin/edit_window.patch b/install/MarcMentat/2023.2/Mentat_bin/edit_window.patch new file mode 100644 index 000000000..915af9bf6 --- /dev/null +++ b/install/MarcMentat/2023.2/Mentat_bin/edit_window.patch @@ -0,0 +1,24 @@ +--- ++++ +@@ -1,18 +1,5 @@ + #!/bin/sh +-# This script opens a window running an editor. The default window is an +-# xterm, and the default editor is vi. These may be customized. ++# This script opens a window running an editor. ++# The command to invoke the editor is specified during DAMASK installation + +-dir= +-for d in /usr/bin /usr/bin/X11; do +- if test -x "$d/xterm"; then +- dir="$d" +- break +- fi +-done +- +-if test -z "$dir"; then +- echo "$0: Could not find xterm" +- exit 1 +-fi +- +-"$dir/xterm" -T "vi $*" -n "vi $*" -e vi $* ++%EDITOR% $* diff --git a/install/MarcMentat/2023.2/Mentat_bin/kill4.patch b/install/MarcMentat/2023.2/Mentat_bin/kill4.patch new file mode 100644 index 000000000..e69de29bb diff --git a/install/MarcMentat/2023.2/Mentat_bin/kill5.patch b/install/MarcMentat/2023.2/Mentat_bin/kill5.patch new file mode 100644 index 000000000..e69de29bb diff --git a/install/MarcMentat/2023.2/Mentat_bin/kill6.patch b/install/MarcMentat/2023.2/Mentat_bin/kill6.patch new file mode 100644 index 000000000..e69de29bb diff --git a/install/MarcMentat/2023.2/Mentat_bin/submit4.patch b/install/MarcMentat/2023.2/Mentat_bin/submit4.patch new file mode 100644 index 000000000..98c51e76d --- /dev/null +++ b/install/MarcMentat/2023.2/Mentat_bin/submit4.patch @@ -0,0 +1,38 @@ +--- ++++ +@@ -63,10 +63,10 @@ + if [ "$slv" != "" -a "$slv" != "marc" -a "$slv" != "datfit" ]; then + slv="-iam sfm" + fi +-if [ "$slv" == "marc" ]; then ++if [ "$slv" = "marc" ]; then + slv="" + fi +-if [ "$slv" == "datfit" ]; then ++if [ "$slv" = "datfit" ]; then + slv="-iam datfit" + fi + +@@ -91,6 +91,7 @@ + srcfile="-u $srcfile -save y" + ;; + runsaved) ++ srcfile=${srcfile%.*}".marc" + srcfile="-prog $srcfile" + ;; + esac +@@ -189,12 +190,12 @@ + unset PYTHONPATH + + if [ "$doe_first" = "-" ]; then # submit of regular Marc job +- "${DIR}/tools/run_marc" $slv -j $job -v n -b y $nprocds $nprocd \ ++ "${DIR}/tools/run_damask_hmp" $slv -j $job -v n -b y $nprocds $nprocd \ + $srcfile $restart $postfile $viewfactorsfile $hostfile \ + $compat $copy_datfile $copy_postfile $scr_dir $dcoup \ + $assem_recov_nthread $nthread $nsolver $mode $gpu > /dev/null 2>&1 + else # submit of a DoE Marc job +- "${DIR}/tools/run_marc" $slv -j $job -v n -b n $nprocds $nprocd \ ++ "${DIR}/tools/run_damask_hmp" $slv -j $job -v n -b n $nprocds $nprocd \ + $srcfile $restart $postfile $viewfactorsfile $hostfile \ + $compat $copy_datfile $copy_postfile $scr_dir $dcoup \ + $assem_recov_nthread $nthread $nsolver $mode $gpu diff --git a/install/MarcMentat/2023.2/Mentat_bin/submit5.patch b/install/MarcMentat/2023.2/Mentat_bin/submit5.patch new file mode 100644 index 000000000..ab32b1058 --- /dev/null +++ b/install/MarcMentat/2023.2/Mentat_bin/submit5.patch @@ -0,0 +1,38 @@ +--- ++++ +@@ -63,10 +63,10 @@ + if [ "$slv" != "" -a "$slv" != "marc" -a "$slv" != "datfit" ]; then + slv="-iam sfm" + fi +-if [ "$slv" == "marc" ]; then ++if [ "$slv" = "marc" ]; then + slv="" + fi +-if [ "$slv" == "datfit" ]; then ++if [ "$slv" = "datfit" ]; then + slv="-iam datfit" + fi + +@@ -91,6 +91,7 @@ + srcfile="-u $srcfile -save y" + ;; + runsaved) ++ srcfile=${srcfile%.*}".marc" + srcfile="-prog $srcfile" + ;; + esac +@@ -189,12 +190,12 @@ + unset PYTHONPATH + + if [ "$doe_first" = "-" ]; then # submit of regular Marc job +- "${DIR}/tools/run_marc" $slv -j $job -v n -b y $nprocds $nprocd \ ++ "${DIR}/tools/run_damask_mp" $slv -j $job -v n -b y $nprocds $nprocd \ + $srcfile $restart $postfile $viewfactorsfile $hostfile \ + $compat $copy_datfile $copy_postfile $scr_dir $dcoup \ + $assem_recov_nthread $nthread $nsolver $mode $gpu > /dev/null 2>&1 + else # submit of a DoE Marc job +- "${DIR}/tools/run_marc" $slv -j $job -v n -b n $nprocds $nprocd \ ++ "${DIR}/tools/run_damask_mp" $slv -j $job -v n -b n $nprocds $nprocd \ + $srcfile $restart $postfile $viewfactorsfile $hostfile \ + $compat $copy_datfile $copy_postfile $scr_dir $dcoup \ + $assem_recov_nthread $nthread $nsolver $mode $gpu diff --git a/install/MarcMentat/2023.2/Mentat_bin/submit6.patch b/install/MarcMentat/2023.2/Mentat_bin/submit6.patch new file mode 100644 index 000000000..d5ea3cfde --- /dev/null +++ b/install/MarcMentat/2023.2/Mentat_bin/submit6.patch @@ -0,0 +1,38 @@ +--- ++++ +@@ -63,10 +63,10 @@ + if [ "$slv" != "" -a "$slv" != "marc" -a "$slv" != "datfit" ]; then + slv="-iam sfm" + fi +-if [ "$slv" == "marc" ]; then ++if [ "$slv" = "marc" ]; then + slv="" + fi +-if [ "$slv" == "datfit" ]; then ++if [ "$slv" = "datfit" ]; then + slv="-iam datfit" + fi + +@@ -91,6 +91,7 @@ + srcfile="-u $srcfile -save y" + ;; + runsaved) ++ srcfile=${srcfile%.*}".marc" + srcfile="-prog $srcfile" + ;; + esac +@@ -189,12 +190,12 @@ + unset PYTHONPATH + + if [ "$doe_first" = "-" ]; then # submit of regular Marc job +- "${DIR}/tools/run_marc" $slv -j $job -v n -b y $nprocds $nprocd \ ++ "${DIR}/tools/run_damask_lmp" $slv -j $job -v n -b y $nprocds $nprocd \ + $srcfile $restart $postfile $viewfactorsfile $hostfile \ + $compat $copy_datfile $copy_postfile $scr_dir $dcoup \ + $assem_recov_nthread $nthread $nsolver $mode $gpu > /dev/null 2>&1 + else # submit of a DoE Marc job +- "${DIR}/tools/run_marc" $slv -j $job -v n -b n $nprocds $nprocd \ ++ "${DIR}/tools/run_damask_lmp" $slv -j $job -v n -b n $nprocds $nprocd \ + $srcfile $restart $postfile $viewfactorsfile $hostfile \ + $compat $copy_datfile $copy_postfile $scr_dir $dcoup \ + $assem_recov_nthread $nthread $nsolver $mode $gpu diff --git a/install/MarcMentat/2023.2/Mentat_menus/job_run.ms.patch b/install/MarcMentat/2023.2/Mentat_menus/job_run.ms.patch new file mode 100644 index 000000000..92ffcf8bd --- /dev/null +++ b/install/MarcMentat/2023.2/Mentat_menus/job_run.ms.patch @@ -0,0 +1,158 @@ +--- ++++ +@@ -362,11 +362,18 @@ + } + button { + position +25 = +- size 25 4 ++ size 18 4 + text "ADVANCED JOB SUBMISSION" + help "job_run#Job Submission And Control" + popmenu job_submit_adv_pm + } ++ button { ++ position +18 = ++ size 7 4 ++ text "DAMASK" ++ help "damask_run#Job Submission And Control" ++ popmenu damask ++ } + button { + position 0 +4 + size 16 4 +@@ -1307,6 +1314,135 @@ + } + + ++#-------------------------------------------------------------------------------------------------- ++popmenu damask { ++ ++#ifdef QT_MENTAT ++ text "DAMASK.MPIE.DE" ++#endif ++ ++ group { ++#ifndef QT_MENTAT ++ label { ++ position 0 0 ++ size 50 4 ++ text "DAMASK.MPIE.DE" ++ } ++#endif ++ ++ label { ++ position 1 6 ++ size 13 6 ++ text "Optimzation" ++ border_width 1 ++ border_color black ++ } ++ ++ label { ++ position +13 = ++ size 20 6 ++ text "write Input" ++ border_width 1 ++ border_color black ++ } ++ ++ label { ++ position +18 = ++ size 30 6 ++ text "do not write Inp." ++ border_width 1 ++ border_color black ++ } ++ ++ label { ++ position -32 +6 ++ size 12 6 ++ text "O3 / OpenMP" ++ border_width 1 ++ border_color black ++ } ++ ++ popdown { ++ position +12 = ++ size 20 6 ++ text "Submit" ++ command "*submit_job 4 *monitor_job" ++ } ++ ++ popdown { ++ position +20 = ++ size 20 6 ++ text "Execute" ++ command "*execute_job 4 *monitor_job" ++ } ++ ++ label { ++ position -32 +6 ++ size 12 6 ++ text "O1 / OpenMP" ++ border_width 1 ++ border_color black ++ } ++ ++ popdown { ++ position +12 = ++ size 20 6 ++ text "Submit" ++ command "*submit_job 5 *monitor_job" ++ } ++ ++ popdown { ++ position +20 = ++ size 20 6 ++ text "Execute" ++ command "*execute_job 5 *monitor_job" ++ } ++ ++ label { ++ position -32 +6 ++ size 12 6 ++ text "O0 / OpenMP" ++ border_width 1 ++ border_color black ++ } ++ ++ popdown { ++ position +12 = ++ size 20 6 ++ text "Submit" ++ command "*submit_job 6 *monitor_job" ++ } ++ ++ popdown { ++ position +20 = ++ size 20 6 ++ text "Execute" ++ command "*execute_job 6 *monitor_job" ++ } ++ ++ popdown { ++ position 19 +8 ++ size 12 8 ++ text "CANCEL" ++ } ++} ++ ++ window { ++ parent mentat ++ origin 38 8 ++#ifdef DCOM ++ size 50 100 ++#else ++ size 50 94 ++#endif ++ background_color body ++ border_width 1 ++ border_color border ++ buffering single ++ } ++ mode permanent ++} ++ + #-------------------------------------------------------------------------------------------------- + popmenu job_exit_msg_pm { + diff --git a/src/Marc/include/concom2023.2 b/src/Marc/include/concom2023.2 new file mode 100644 index 000000000..df42413cf --- /dev/null +++ b/src/Marc/include/concom2023.2 @@ -0,0 +1,473 @@ +! common block definition file taken from respective MSC.Marc release and reformated to free format +!*********************************************************************** +! +! File: concom.cmn +! +! MSC.Marc include file +! +integer & + iacous, iasmbl, iautth, ibear, icompl, iconj, icreep, ideva, idyn, idynt,& + ielas, ielcma, ielect, iform, ifour, iharm, ihcps, iheat, iheatt, ihresp,& + ijoule, ilem, ilnmom, iloren, inc, incext, incsub, ipass, iplres, ipois,& + ipoist, irpflo, ismall, ismalt, isoil, ispect, ispnow, istore, iswep, ithcrp,& + itherm, iupblg, iupdat, jacflg, jel, jparks, largst, lfond, loadup, loaduq,& + lodcor, lovl, lsub, magnet, ncycle, newtnt, newton, noshr, linear, ivscpl,& + icrpim, iradrt, ipshft, itshr, iangin, iupmdr, iconjf, jincfl, jpermg, jhour,& + isolvr, jritz, jtable, jshell, jdoubl, jform, jcentr, imini, kautth, iautof,& + ibukty, iassum, icnstd, icnstt, kmakmas, imethvp, iradrte, iradrtp, iupdate, iupdatp,& + ncycnt, marmen , idynme, ihavca, ispf, kmini, imixex, largtt, kdoela, iautofg,& + ipshftp, idntrc, ipore, jtablm, jtablc, isnecma, itrnspo, imsdif, jtrnspo, mcnear,& + imech, imecht, ielcmat, ielectt, magnett, imsdift, noplas, jtabls, jactch, jtablth,& + kgmsto , jpzo, ifricsh, iremkin, iremfor, ishearp, jspf, machining, jlshell, icompsol,& + iupblgfo, jcondir, nstcrp, nactive, ipassref, nstspnt, ibeart, icheckmpc, noline, icuring,& + ishrink, ioffsflg, isetoff, ioffsetm,iharmt, inc_incdat, iautspc, ibrake, icbush, istream_input,& + iprsinp, ivlsinp, ifirst_time,ipin_m, jgnstr_glb, imarc_return,iqvcinp, nqvceid, istpnx, imicro1,& + iaxisymm, jbreakglue,iglstif, jfastasm,iwear, iwearcf, imixmeth, ielcmadyn, idinout, igena_meth,& + magf_meth, non_assumed, iredoboudry, ioffsz0,icomplt, mesh_dual, iactrp, mgnewton, iusedens,igsigd0,& + iaem, icosim, inodels, nlharm, iampini, iphasetr, inonlcl, inonlct, iforminp,ispecerror,& + icsprg, imol, imolt, idatafit,iharmpar, inclcase, imultifreq,init_elas, ifatig, iftgmat,& + nchybrid, ibuckle, iexpande, matfor, ishormem, imsdifths +dimension :: ideva(60) +integer num_concom +parameter(num_concom=266) +common/marc_concom/& + iacous, iasmbl, iautth, ibear, icompl, iconj, icreep, ideva, idyn, idynt,& + ielas, ielcma, ielect, iform, ifour, iharm, ihcps, iheat, iheatt, ihresp,& + ijoule, ilem, ilnmom, iloren, inc, incext, incsub, ipass, iplres, ipois,& + ipoist, irpflo, ismall, ismalt, isoil, ispect, ispnow, istore, iswep, ithcrp,& + itherm, iupblg, iupdat, jacflg, jel, jparks, largst, lfond, loadup, loaduq,& + lodcor, lovl, lsub, magnet, ncycle, newtnt, newton, noshr, linear, ivscpl,& + icrpim, iradrt, ipshft, itshr, iangin, iupmdr, iconjf, jincfl, jpermg, jhour,& + isolvr, jritz, jtable, jshell, jdoubl, jform, jcentr, imini, kautth, iautof,& + ibukty, iassum, icnstd, icnstt, kmakmas, imethvp, iradrte, iradrtp, iupdate, iupdatp,& + ncycnt, marmen, idynme, ihavca, ispf, kmini, imixex, largtt, kdoela, iautofg,& + ipshftp, idntrc, ipore, jtablm, jtablc, isnecma, itrnspo, imsdif, jtrnspo, mcnear,& + imech, imecht, ielcmat, ielectt, magnett, imsdift, noplas, jtabls, jactch, jtablth,& + kgmsto , jpzo, ifricsh, iremkin, iremfor, ishearp, jspf, machining, jlshell, icompsol,& + iupblgfo, jcondir, nstcrp, nactive, ipassref, nstspnt, ibeart, icheckmpc, noline, icuring,& + ishrink, ioffsflg, isetoff, ioffsetm,iharmt, inc_incdat, iautspc, ibrake, icbush, istream_input,& + iprsinp, ivlsinp, ifirst_time,ipin_m, jgnstr_glb, imarc_return,iqvcinp, nqvceid, istpnx, imicro1,& + iaxisymm, jbreakglue,iglstif, jfastasm,iwear, iwearcf, imixmeth, ielcmadyn, idinout, igena_meth,& + magf_meth, non_assumed, iredoboudry, ioffsz0,icomplt, mesh_dual, iactrp, mgnewton, iusedens,igsigd0,& + iaem, icosim, inodels, nlharm, iampini, iphasetr, inonlcl, inonlct, iforminp,ispecerror,& + icsprg, imol, imolt, idatafit,iharmpar, inclcase, imultifreq,init_elas, ifatig, iftgmat,& + nchybrid, ibuckle, iexpande, matfor, ishormem, imsdifths +! +! comments of variables: +! +! iacous Control flag for acoustic analysis. Input data. +! iacous=1 modal acoustic analysis. +! iacous=2 harmonic acoustic-structural analysis. +! iasmbl Control flag to indicate that operator matrix should be +! recalculated. +! iautth Control flag for AUTO THERM option. +! ibear Control flag for bearing analysis. Input data. +! icompl Control variable to indicate that a complex analysis is +! being performed. Either a Harmonic analysis with damping, +! or a harmonic electro-magnetic analysis. Input data. +! iconj Flag for EBE conjugate gradient solver (=solver 1, retired) +! Also used for VKI iterative solver. +! icreep Control flag for creep analysis. Input data. +! ideva(60) - debug print out flag +! 1 print element stiffness matrices, mass matrix +! 2 output matrices used in tying +! 3 force the solution of a nonpositive definite matrix +! 4 print info of connections to each node +! 5 info of gap convergence, internal heat generated, contact +! touching and separation +! 6 nodal value array during rezoning +! 7 tying info in CONRAD GAP option, fluid element numbers in +! CHANNEL option +! 8 output incremental displacements in local coord. system +! 9 latent heat output +! 10 stress-strain in local coord. system +! 11 additional info on interlaminar stress +! 12 output right hand side and solution vector +! 13 info of CPU resources used and memory available on NT +! 14 info of mesh adaption process, 2D outline information +! info of penetration checking for remeshing +! save .fem files after afmesh3d meshing +! print local adaptivity info +! 15 surface energy balance flag +! 16 print info regarding pyrolysis +! 17 print info of "streamline topology" +! 18 print mesh data changes after remeshing +! 19 print material flow stress data read in from *.mat file +! if unit flag is on, print out flow stress after conversion +! 20 print information on table input +! 21 print out information regarding kinematic boundary conditions +! 22 print out information regarding dist loads, point loads, film +! and foundations +! 23 print out information about automatic domain decomposition +! 24 print out iteration information in SuperForm status report file +! 25 print out information for ablation +! 26 print out information for films - Table input +! 27 print out the tying forces +! 28 print out for CASI solver, convection, +! 29 DDM single file debug printout +! 30 print out cavity debug info +! 31 print out welding related info +! 32 prints categorized DDM memory usage +! 33 print out the cutting info regarding machining feature +! 34 print out the list of quantities which can be defined via a table +! and for each quantity the supported independent variables +! 35 print out detailed coupling region info +! 36 print out solver debug info level 1 (Least Detailed) +! 37 print out solver debug info level 1 (Medium Detailed) +! 38 print out solver debug info level 1 (Very Detailed) +! 39 print detailed memory allocation info +! 40 print out marc-adams debug info +! 41 output rezone mapping post file for debugging +! 42 output post file after calling oprofos() for debugging +! 43 debug printout for vcct +! 44 debug printout for progressive failure +! 45 print out automatically generated midside node coordinates (arecrd) +! 46 print out message about routine and location, where the ibort is raised (ibort_inc) +! 47 print out summary message of element variables on a +! group-basis after all the automatic changes have been +! made (em_ellibp) +! 48 Automatically generate check results based on max and min vals. +! These vals are stored in the checkr file, which is inserted +! into the *dat file by the generate_check_results script from /marc/tools +! 49 Automatically generate check results based on the real calculated values +! at the sppecified check result locations. +! These vals are stored in the checkr file, which is inserted +! into the *dat file by the update_check_results script from /marc/tools +! 50 generate a file containing the resistance or capacity matrix; +! this file can be used to compare results with a reference file +! 51 print out detailed information for segment-to-segment contact +! 52 print out detailed relative displacement information +! for uniaxial sliding contact +! 53 print out detailed sliding direction information for +! uniaxial sliding contact +! 54 print out detailed information for edges attached to a curve +! 55 print information related to viscoelasticity calculations +! 56 print out detailed information for element coloring for multithreading +! 57 print out extra overheads due to multi-threading. +! These overhead includes (i) time and (ii) memory. +! The memory report will be summed over all the children. +! 58 debug output for ELSTO usage +! 59 print out contact body forces and nodes in contact +! +! idyn Control flag for dynamics. Input data. +! 1 = eigenvalue extraction and / or modal superposition +! 2 = Newmark Beta and Single Step Houbolt (ssh with idynme=1) +! 3 = Houbolt +! 4 = Central difference +! 5 = Newer central difference +! idynt Copy of idyn at begining of increment +! ielas Control flag for ELASTIC analysis. Input data. +! Set by user or automatically turned on by Fourier option. +! Implies that each load case is treated separately. +! In Adaptive meshing analysis , forces re-analysis until +! convergence obtained. +! Also seriously misused to indicate no convergence. +! = 1 elastic option with fourier analysis +! = 2 elastic option without fourier analysis +! =-1 no convergence in recycles or max # increments reached +! Set to 1 if ELASTIC or SUBSTRUC parameter cards are used, +! or if fourier option is used. +! Then set to 2 if not fourier analysis. +! ielcma Control flag for electromagnetic analysis. Input data. +! ielcma = 1 Harmonic formulation +! ielcma = 2 Transient formulation +! ielect Control flag for electrostatic option. Input data. +! iform Control flag indicating that contact will be performed. +! ifour Control flag for Fourier analysis. +! 0 = Odd and even terms. +! 1 = symmetric (cosine) terms +! 2 = antisymmetric (sine) terms. +! iharm Control flag to indicate that a harmonic analysis will +! be performed. May change between passes. +! ihcps Control flag for coupled thermal - stress analysis. +! iheat Control flag for heat transfer analysis. Input data. +! iheatt Permanent control flag for heat transfer analysis. +! Note in coupled analysis iheatt will remain as one, +! but iheat will be zero in stress pass. +! ihresp Control flag to indicate to perform a harmonic subincrement. +! ijoule Control flag for Joule heating. +! ilem Control flag to determin which vector is to be transformed. +! Control flag to see where one is: +! ilem = 1 - elem.f +! ilem = 2 - initst.f +! ilem = 3 - pressr.f +! ilem = 3 - fstif.f +! ilem = 4 - jflux.f +! ilem = 4 - strass.f +! ilem = 5 - mass.f +! ilem = 5 - osolty.f +! ilnmom Control flag for soil - pore pressure calculation. Input data. +! ilnmom = 0 - perform only pore pressure calculation. +! = 1 - couples pore pressure - displacement analysis +! iloren Control flag for DeLorenzi J-Integral evaluation. Input data. +! inc Increment number. +! incext Control flag indicating that currently working on a +! subincrement. +! Could be due to harmonics , damping component (bearing), +! stiffness component (bearing), auto therm creep or +! old viscoplaticity +! incsub Sub-increment number. +! inonlcl control flag for nonlocal pass +! inonlct permanent control flag for nonlocal pass +! ipass Control flag for which part of coupled analysis. +! ipass = -1 - reset to base values +! ipass = 0 - do nothing +! ipass = 1 - stress part +! ipass = 2 - heat transfer part +! 3 - fluid pass +! 4 - joule heating pass +! 5 - pore pressure pass +! 6 - electrostatic pass +! 7 - magnetostatic pass +! 8 - electromagnetic pass +! 9 - diffusion pass +! ipass = 10 - nonlocal part +! iplres Flag indicating that either second matrix is stored. +! dynamic analysis - mass matrix +! heat transfer - specific heat matrix +! buckle - initial stress stiffness +! ipois Control flag indicating Poisson type analysis +! ipois = 1 for heat transfer +! = 1 for heat transfer part of coupled +! = 1 for bearing +! = 1 for electrostatic +! = 1 for magnetostatic +! = 1 for nonlocal part +! ipoist Permanent copy of ipois. In coupled analysis , ipois = 0 +! in stress portion, yet ipoist will still =1. +! irpflo global flag for rigid plastic flow analysis +! = 1 eularian formulation +! = 2 regular formulation; rigid material present in the analysis +! ismall control flag to indicate small displacement analysis. input data. +! ismall = 0 - large disp included. +! ismall = 1 - small displacement. +! the flag is changing between passes. +! ismalt permanent copy of ismall . in heat transfer portion of +! coupled analysis ismall =0 , but ismalt remains the same. +! isoil control flag indicating that soil / pore pressure +! calculation . input data. +! ispect control flag for response spectrum calculation. input data. +! ispnow control flag to indicate to perform a spectrum response +! calculation now. +! istore store stresses flag. +! istore = 0 in elem.f and if first pass of creep +! convergence checking in ogetst.f +! or harmonic analysis or thruc.f if not +! converged. +! iswep control flag for eigenvalue analysis. +! iswep=1 - go do extraction process +! ithcrp control flag for auto therm creep option. input data. +! itherm control flag for either temperature dependent material +! properties and/or thermal loads. +! iupblg control flag for follower force option. input data. +! iupdat control flag for update lagrange option for current element. +! jacflg control flag for lanczos iteration method. input data. +! jel control flag indicating that total load applied in +! increment, ignore previous solution. +! jel = 1 in increment 0 +! = 1 if elastic or fourier +! = 1 in subincrements with elastic and adaptive +! jparks control flag for j integral by parks method. input data. +! largst control flag for finite strain plasticity. input data. +! lfond control variable that indicates if doing elastic +! foundation or film calculation. influences whether +! this is volumetric or surface integration. +! loadup control flag that indicates that nonlinearity occurred +! during previous increment. +! loaduq control flag that indicates that nonlinearity occurred. +! lodcor control flag for switching on the residual load correction. +! notice in input stage lodcor=0 means no loadcor, +! after omarc lodcor=1 means no loadcor +! lovl control flag for determining which "overlay" is to +! be called from ellib. +! lovl = 1 omarc +! = 2 oaread +! = 3 opress +! = 4 oasemb +! = 5 osolty +! = 6 ogetst +! = 7 oscinc +! = 8 odynam +! = 9 opmesh +! = 10 omesh2 +! = 11 osetz +! = 12 oass +! = 13 oincdt +! = 14 oasmas +! = 15 ofluas +! = 16 ofluso +! = 17 oshtra +! = 18 ocass +! = 19 osoltc +! = 20 orezon +! = 21 otest +! = 22 oeigen +! lsub control variable to determine which part of element +! assembly function is being done. +! lsub = 1 - no longer used +! = 2 - beta* +! = 3 - cons* +! = 4 - ldef* +! = 5 - posw* +! = 6 - theta* +! = 7 - tmarx* +! = 8 - geom* +! magnet control flag for magnetostatic analysis. input data. +! ncycle cycle number. accumulated in osolty.f +! note first time through oasemb.f , ncycle = 0. +! newtnt control flag for permanent copy of newton. +! newton iteration type. input data. +! newton : = 1 full newton raphson +! 2 modified newton raphson +! 3 newton raphson with strain correct. +! 4 direct substitution +! 5 direct substitution followed by n.r. +! 6 direct substitution with line search +! 7 full newton raphson with secant initial stress +! 8 secant method +! 9 full newton raphson with line search +! noshr control flag for calculation interlaminar shears for +! elements 22,45, and 75. input data. +!ees +! +! jactch = 1 or 2 if elements are activated or deactivated +! = 3 if elements are adaptively remeshed or rezoned +! = 0 normally / reset to 0 when assembly is done +! ifricsh = 0 call to fricsh in otest not needed +! = 1 call to fricsh (nodal friction) in otest needed +! iremkin = 0 remove deactivated kinematic boundary conditions +! immediately - only in new input format (this is default) +! = 1 remove deactivated kinematic boundary conditions +! gradually - only in new input format +! iremfor = 0 remove force boundary conditions immediately - +! only in new input format (this is default) +! = 1 remove force boundary conditions gradually - +! only in new input format (this is default) +! ishearp set to 1 if shear panel elements are present in the model +! +! jspf = 0 not in spf loadcase +! > 0 in spf loadcase (jspf=1 during first increment) +! machining = 1 if the metal cutting feature is used, for memory allocation purpose +! = 0 (default) if no metal cutting feature required +! +! jlshell = 1 if there is a shell element in the mesh +! icompsol = 1 if there is a composite solid element in the mesh +! iupblgfo = 1 if follower force for point loads +! jcondir = 1 if contact priority option is used +! nstcrp = 0 (default) steady state creep flag (undocumented feature. +! if not 0, turns off special ncycle = 0 code in radial.f) +! nactive = number of active passes, if =1 then it's not a coupled analysis +! ipassref = reference ipass, if not in a multiphysics pass ipass=ipassref +! icheckmpc = value of mpc-check parameter option +! noline = set to 1 in osolty if no line seacrh should be done in ogetst +! icuring = set to 1 if the curing is included for the heat transfer analysis. +! ishrink = set to 1 if shrinkage strain is included for mechancial analysis. +! ioffsflg = 1 for small displacement beam/shell offsets +! = 2 for large displacement beam/shell offsets +! isetoff = 0 - do not apply beam/shell offsets +! = 1 - apply beam/shell offsets +! ioffsetm = min. value of offset flag +! iharmt = 1 global flag if a coupled analysis contains an harmonic pass +! inc_incdat = flag to record increment number of a new loadcase in incdat.f +! iautspc = flag for AutoSPC option +! ibrake = brake squeal in this increment +! icbush = set to 1 if cbush elements present in model +! istream_input = set to 1 for streaming input calling Marc as library +! iprsinp = set to 1 if pressure input, introduced so other variables +! such as h could be a function of pressure +! ivlsinp = set to 1 if velocity input, introduced so other variables +! such as h could be a function of velocity +! ipin_m = # of beam element with PIN flag +! jgnstr_glb = global control over pre or fast integrated composite shells +! imarc_return = Marc return flag for streaming input control +! iqvcimp = if non-zero, then the number of QVECT boundary conditions +! nqvceid = number of QVECT boundary conditions, where emisivity/absorbtion id entered +! istpnx = 1 if to stop at end of increment +! imicro1 = 1 if micro1 interface is used +! iaxisymm = set to 1 if axisymmetric analysis +! jbreakglue = set to 1 if breaking glued option is used +! iglstif = 1 if ddm and global stiffness matrix formed (sgi solver 6 or solver9) +! jfastasm = 1 do fast assembly using SuperForm code +! iwear = set to 1 if wear model, set to 2 if wear model and coordinates updated +! iwearcf = set to 1 to store nodal coefficient of friction for wear calculation +! imixmeth = set=1 then use nonlinear mixture material - allocate memory +! ielcmadyn = flag for magnetodynamics +! 0 - electromagnetics using newmark beta +! 1 - transient magnetics using backward euler +! idinout = flag to control if inside out elements should be deactivated +! igena_meth = 0 - generalized alpha parameters depend on whether or not contact +! is flagged (dynamic,7) +! 10 - generalized alpha parameters are optimized for a contact +! analysis (dynamic,8) +! 11 - generalized alpha parameters are optimized for an analysis +! without contact (dynamic,8) +! magf_meth = - Method to compute force in magnetostatic - structural +! = 1 - Virtual work method based on finite difference for the force computation +! = 2 - Maxwell stress tensor +! = 3 - Virtual work method based on local derivative for the force computation +! non_assumed = 1 no assumed strain formulation (forced) +! iredoboudry set to 1 if contact boundary needs to be recalculated +! ioffsz0 = 1 if composite are used with reference position.ne.0 +! icomplt = 1 global flag if a coupled analysis contains an complex pass +! mesh_dual = 1 two independent meshes are used in magnetodynamic/thermal/structural +! one for magnetodynamic and the other for the remaining passes +! iactrp = 1 in an analysis with global remeshing, include inactive +! rigid bodies on post file +! mgnewton = 1 Use full Newton Raphson iteration for magnetostatic pass +! +! iusedens > 0 if mass density is used in the analysis (dynamics, mass dependent loading) +! igsigd0 = 1 set varselem(igsigd) to zero in next oasemb +! iaem = 1 if marc is called from aem (0 - off - default) +! icosim = 1 if marc is used in co-simulation analysis with ADAMS using the CosimEngine +! = 2 if marc is used in co-simulation analysis with ADAMS using the ACSI interface +! = 3 if marc is used in co-simulation analysis with scFLOW using the CosimEngine +! = 4 if marc is used in co-simulation analysis with scFLOW and ADAMS using the CosimEngine +! inodels = 1 nodal integration elements 239/240/241 present +! nlharm = 0 harmonic subincrements are linear +! = 1 harmonic subincrements are nonlinear +! iampini = 0 amplitude of previous harmonic subinc is initial estimate (default) +! = 1 zero amplitude is initial estimate +! iphasetr = 1 phase transformation material model is used +! iforminp flag indicating that contact is switched on via the CONTACT +! option in the input file (as opposed to the case that contact +! is switched on internally due to cyclic symmetry or model +! section creation) +! ispecerror = a+10*b (only for spectrum response analysis with missing mass option) +! a=0 or a=1 (modal shape with non-zero shift) +! b=0 or b=1 (recover with new assembly of stiffness matrix) +! icsprg = set to 1 if spring elements present in model +! imol Control flag for molecualr diffusion pass +! imolt Permanent control flag for molecualr diffusion pass +! Note in coupled analysis imolt will remain as one, +! but imol will be zero in stress pass or thermal pass. +! idatafit = run Marc to fit parameters +! iharmpar = 1 if harmonic parameter option is used +! inclcase load case increment use for cyclic plasticity data fitting +! imultifreq flag to indicate how many harmonic magnetodynamic passes are computed in coupled +! magnetodynamic/thermal(/structural) analyses. +! 0 or 1 one pass 2 two passes 3 or more is not supported +! init_elas use elastic stress-strain law as the material tangent for +! the first cycle of an increment +! ifatig packed integer telling which fatigue mode is active +! 1 = elastomer +! 10 = stress-life +! 100 = strain-life +! = 2 strain-life fatigue +! iftgmat = 0 no fatigue material properties in the dat file +! = 1 fatigue material properties in the dat file +! nchybrid cycle count used for hybrid contact; meant to force an extra iteration +! if the overlap for a node in hybrid contact is too large +! ibuckle buckle parameter option is active +! iexpande set to 1 if expanded elements (248, 249, 250 or 251) are +! present, 0 otherwise +! matfor flag for material forces computation +! 0: Eshleby stress and material force vector not requested +! 1: output Eshelby stress tensor, but no material force vector +! 2: output material force vector, but no Eshelby stress tensor +! 3: output Eshelby stress tensor and material force vector +! ishormem set to 1 if shell or membrane elements are present, 0 otherwise +! imsdifths flag for diffusion(pressure)/thermal/structural analysis +! +!*********************************************************************** +!$omp threadprivate(/marc_concom/) +!! diff --git a/src/Marc/include/creeps2023.2 b/src/Marc/include/creeps2023.2 new file mode 100644 index 000000000..b35d2b6bf --- /dev/null +++ b/src/Marc/include/creeps2023.2 @@ -0,0 +1,73 @@ +! common block definition file taken from respective MSC.Marc release and reformated to free format +!*********************************************************************** +! +! File: creeps.cmn +! +! MSC.Marc include file +! +real(pReal) cptim,timinc,timinc_p,timinc_s,timincm,timinc_a,timinc_b +integer icfte,icfst,icfeq,icftm,icetem,mcreep,jcreep,icpa,icftmp,icfstr,& + icfqcp,icfcpm,icrppr,icrcha,icpb,iicpmt,iicpa +real(pReal) time_beg_lcase,time_beg_inc,fractol,time_beg_pst +real(pReal) fraction_donn,timinc_ol2 +! +integer num_creepsr,num_creepsi,num_creeps2r,ncrp_arry +parameter(num_creepsr=7) +parameter(num_creepsi=17) +parameter(num_creeps2r=6) +parameter(ncrp_arry=7) +common/marc_creeps/cptim,timinc,timinc_p,timinc_s,timincm,timinc_a,timinc_b,icfte,icfst,& + icfeq,icftm,icetem,mcreep,jcreep,icpa,icftmp,icfstr,icfqcp,icfcpm,icrppr,icrcha,icpb,iicpmt,iicpa +common/marc_creeps2/time_beg_lcase,time_beg_inc,fractol,time_beg_pst,fraction_donn,timinc_ol2 +! +! cptim Total time at begining of increment. +! timinc Incremental time for this step. +! icfte Local copy number of slopes of creep strain rate function +! versus temperature. Is -1 if exponent law used. +! icfst Local copy number of slopes of creep strain rate function +! versus equivalent stress. Is -1 if exponent law used. +! icfeq Local copy number of slopes of creep strain rate function +! versus equivalent strain. Is -1 if exponent law used. +! icftm Local copy number of slopes of creep strain rate function +! versus time. Is -1 if exponent law used. +! icetem Element number that needs to be checked for creep convergence +! or, if negative, the number of elements that need to +! be checked. In the latter case the elements to check +! are stored in ielcp. +! mcreep Maximum nuber of iterations for explicit creep. +! jcreep Counter of number of iterations for explicit creep +! procedure. jcreep must be .le. mcreep +! icpa(1-6) Pointer to constants in creep strain rate expression. +! icftmp Pointer to temperature dependent creep strain rate data. +! icfstr Pointer to equivalent stress dependent creep strain rate data. +! icfqcp Pointer to equivalent creep strain dependent creep strain +! rate data. +! icfcpm Pointer to equivalent creep strain rate dependent +! creep strain rate data. +! icrppr Permanent copy of icreep +! icrcha Control flag for creep convergence checking , if set to +! 1 then testing on absolute change in stress and creep +! strain, not relative testing. Input data. +! icpb(1-4) Pointer to storage of material id cross reference numbers. +! iicpmt creep law type ID +! =1 - power law +! =2 - solder +! =3 - steady-creep +! =4 - hyperbolic steady-creep +! iicpa Pointer to table IDs for constants in creep strain rate +! expression +! +! +! time_beg_lcase time at the beginning of the current load case +! time_beg_inc time at the beginning of the current increment +! fractol fraction of loadcase or increment time when we +! consider it to be finished +! time_beg_pst time corresponding to first increment to be +! read in from thermal post file for auto step +! +! timinc_old Time step of the previous increment +! +!*********************************************************************** +!!$omp threadprivate(/marc_creeps/) +!!$omp threadprivate(/marc_creeps2/) +!! From 7eb16ddc75e84d723ded319221b21b93d63ac2d1 Mon Sep 17 00:00:00 2001 From: Franz Roters Date: Wed, 18 Oct 2023 13:54:15 +0200 Subject: [PATCH 07/84] add support for Marc2023.3 --- .../2023.3/Marc_tools/comp_damask_hmp.patch | 49 ++ .../2023.3/Marc_tools/comp_damask_lmp.patch | 49 ++ .../2023.3/Marc_tools/comp_damask_mp.patch | 49 ++ .../2023.3/Marc_tools/include_linux64.patch | 75 +++ .../2023.3/Marc_tools/run_damask_hmp.patch | 477 ++++++++++++++++++ .../2023.3/Marc_tools/run_damask_lmp.patch | 477 ++++++++++++++++++ .../2023.3/Marc_tools/run_damask_mp.patch | 477 ++++++++++++++++++ .../2023.3/Mentat_bin/edit_window.patch | 24 + .../MarcMentat/2023.3/Mentat_bin/kill4.patch | 0 .../MarcMentat/2023.3/Mentat_bin/kill5.patch | 0 .../MarcMentat/2023.3/Mentat_bin/kill6.patch | 0 .../2023.3/Mentat_bin/submit4.patch | 25 + .../2023.3/Mentat_bin/submit5.patch | 25 + .../2023.3/Mentat_bin/submit6.patch | 25 + .../2023.3/Mentat_menus/job_run.ms.patch | 158 ++++++ src/Marc/include/concom2023.3 | 473 +++++++++++++++++ src/Marc/include/creeps2023.3 | 73 +++ 17 files changed, 2456 insertions(+) create mode 100644 install/MarcMentat/2023.3/Marc_tools/comp_damask_hmp.patch create mode 100644 install/MarcMentat/2023.3/Marc_tools/comp_damask_lmp.patch create mode 100644 install/MarcMentat/2023.3/Marc_tools/comp_damask_mp.patch create mode 100644 install/MarcMentat/2023.3/Marc_tools/include_linux64.patch create mode 100644 install/MarcMentat/2023.3/Marc_tools/run_damask_hmp.patch create mode 100644 install/MarcMentat/2023.3/Marc_tools/run_damask_lmp.patch create mode 100644 install/MarcMentat/2023.3/Marc_tools/run_damask_mp.patch create mode 100644 install/MarcMentat/2023.3/Mentat_bin/edit_window.patch create mode 100644 install/MarcMentat/2023.3/Mentat_bin/kill4.patch create mode 100644 install/MarcMentat/2023.3/Mentat_bin/kill5.patch create mode 100644 install/MarcMentat/2023.3/Mentat_bin/kill6.patch create mode 100644 install/MarcMentat/2023.3/Mentat_bin/submit4.patch create mode 100644 install/MarcMentat/2023.3/Mentat_bin/submit5.patch create mode 100644 install/MarcMentat/2023.3/Mentat_bin/submit6.patch create mode 100644 install/MarcMentat/2023.3/Mentat_menus/job_run.ms.patch create mode 100644 src/Marc/include/concom2023.3 create mode 100644 src/Marc/include/creeps2023.3 diff --git a/install/MarcMentat/2023.3/Marc_tools/comp_damask_hmp.patch b/install/MarcMentat/2023.3/Marc_tools/comp_damask_hmp.patch new file mode 100644 index 000000000..886ebf008 --- /dev/null +++ b/install/MarcMentat/2023.3/Marc_tools/comp_damask_hmp.patch @@ -0,0 +1,49 @@ +--- ++++ +@@ -6,18 +6,27 @@ + DIR=$1 + user=$3 + program=$4 ++usernoext=$user ++usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f` ++usernoext=`dirname $usernoext`/`$BASENAME $usernoext .F` ++usernoext=`dirname $usernoext`/`$BASENAME $usernoext .for` ++usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f90` ++ ++# add BLAS options for linking ++ BLAS="%BLAS%" ++ + . $DIR/tools/include + DIRJOB=$2 + cd $DIRJOB +-echo "Compiling and linking user subroutine $user.f on host `hostname`" ++echo "Compiling and linking user subroutine $user on host `hostname`" + echo "program: $program" +- $FORTRAN $user.f || \ ++ $DFORTHIGHMP $user || \ + { +- echo "$0: compile failed for $user.f" ++ echo "$0: compile failed for $user" + exit 1 + } + /bin/rm $program 2>/dev/null +- userobj=$user.o ++ userobj=$usernoext.o + + + $LOAD ${program} $DIR/lib/main.o\ +@@ -33,9 +42,13 @@ + $TKLIBS \ + $MRCLIBS \ + $METISLIBS \ ++ $BLAS \ + $SYSLIBS || \ + { +- echo "$0: link failed for $user.o on host `hostname`" ++ echo "$0: link failed for $usernoext.o on host `hostname`" + exit 1 + } + /bin/rm $userobj ++ /bin/rm $DIRJOB/*.mod ++ /bin/rm $DIRJOB/*.smod ++ /bin/rm $DIRJOB/*_genmod.f90 diff --git a/install/MarcMentat/2023.3/Marc_tools/comp_damask_lmp.patch b/install/MarcMentat/2023.3/Marc_tools/comp_damask_lmp.patch new file mode 100644 index 000000000..191cb1a53 --- /dev/null +++ b/install/MarcMentat/2023.3/Marc_tools/comp_damask_lmp.patch @@ -0,0 +1,49 @@ +--- ++++ +@@ -6,18 +6,27 @@ + DIR=$1 + user=$3 + program=$4 ++usernoext=$user ++usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f` ++usernoext=`dirname $usernoext`/`$BASENAME $usernoext .F` ++usernoext=`dirname $usernoext`/`$BASENAME $usernoext .for` ++usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f90` ++ ++# add BLAS options for linking ++ BLAS="%BLAS%" ++ + . $DIR/tools/include + DIRJOB=$2 + cd $DIRJOB +-echo "Compiling and linking user subroutine $user.f on host `hostname`" ++echo "Compiling and linking user subroutine $user on host `hostname`" + echo "program: $program" +- $FORTRAN $user.f || \ ++ $DFORTRANLOWMP $user || \ + { +- echo "$0: compile failed for $user.f" ++ echo "$0: compile failed for $user" + exit 1 + } + /bin/rm $program 2>/dev/null +- userobj=$user.o ++ userobj=$usernoext.o + + + $LOAD ${program} $DIR/lib/main.o\ +@@ -33,9 +42,13 @@ + $TKLIBS \ + $MRCLIBS \ + $METISLIBS \ ++ $BLAS \ + $SYSLIBS || \ + { +- echo "$0: link failed for $user.o on host `hostname`" ++ echo "$0: link failed for $usernoext.o on host `hostname`" + exit 1 + } + /bin/rm $userobj ++ /bin/rm $DIRJOB/*.mod ++ /bin/rm $DIRJOB/*.smod ++ /bin/rm $DIRJOB/*_genmod.f90 diff --git a/install/MarcMentat/2023.3/Marc_tools/comp_damask_mp.patch b/install/MarcMentat/2023.3/Marc_tools/comp_damask_mp.patch new file mode 100644 index 000000000..7c9cf7ba7 --- /dev/null +++ b/install/MarcMentat/2023.3/Marc_tools/comp_damask_mp.patch @@ -0,0 +1,49 @@ +--- ++++ +@@ -6,18 +6,27 @@ + DIR=$1 + user=$3 + program=$4 ++usernoext=$user ++usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f` ++usernoext=`dirname $usernoext`/`$BASENAME $usernoext .F` ++usernoext=`dirname $usernoext`/`$BASENAME $usernoext .for` ++usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f90` ++ ++# add BLAS options for linking ++ BLAS="%BLAS%" ++ + . $DIR/tools/include + DIRJOB=$2 + cd $DIRJOB +-echo "Compiling and linking user subroutine $user.f on host `hostname`" ++echo "Compiling and linking user subroutine $user on host `hostname`" + echo "program: $program" +- $FORTRAN $user.f || \ ++ $DFORTRANMP $user || \ + { +- echo "$0: compile failed for $user.f" ++ echo "$0: compile failed for $user" + exit 1 + } + /bin/rm $program 2>/dev/null +- userobj=$user.o ++ userobj=$usernoext.o + + + $LOAD ${program} $DIR/lib/main.o\ +@@ -33,9 +42,13 @@ + $TKLIBS \ + $MRCLIBS \ + $METISLIBS \ ++ $BLAS \ + $SYSLIBS || \ + { +- echo "$0: link failed for $user.o on host `hostname`" ++ echo "$0: link failed for $usernoext.o on host `hostname`" + exit 1 + } + /bin/rm $userobj ++ /bin/rm $DIRJOB/*.mod ++ /bin/rm $DIRJOB/*.smod ++ /bin/rm $DIRJOB/*_genmod.f90 diff --git a/install/MarcMentat/2023.3/Marc_tools/include_linux64.patch b/install/MarcMentat/2023.3/Marc_tools/include_linux64.patch new file mode 100644 index 000000000..f9646b240 --- /dev/null +++ b/install/MarcMentat/2023.3/Marc_tools/include_linux64.patch @@ -0,0 +1,75 @@ +--- ++++ +@@ -172,6 +178,15 @@ + MARC_COSIM_LIB="$MSCCOSIM_HOME/CoSim$MSCCOSIM_VERSION/Dcosim$MSCCOSIM_VERSION/lib" + fi + ++# DAMASK uses the HDF5 compiler wrapper around the Intel compiler ++H5FC=$(h5fc -shlib -show) ++if [[ "$H5FC" == *"$dir is"* ]]; then ++ H5FC=$(echo $(echo "$H5FC" | tail -n1) | sed -e "s/\-shlib/-fPIC -qopenmp/g") ++ H5FC=${H5FC%-lmpifort*} ++fi ++HDF5_LIB=${H5FC//*ifort/} ++FCOMP="$H5FC" ++ + # AEM + if test "$MARCDLLOUTDIR" = ""; then + DLLOUTDIR="$MARC_LIB" +@@ -599,7 +609,7 @@ + PROFILE=" $PROFILE -pg" + fi + +-FORT_OPT="-c -assume byterecl -safe-cray-ptr -mp1 -WB -fp-model source" ++FORT_OPT="-c -implicitnone -stand f18 -standard-semantics -assume nostd_mod_proc_name -safe-cray-ptr -mp1 -WB -fp-model source" + if test "$MTHREAD" = "OPENMP" + then + FORT_OPT=" $FORT_OPT -qopenmp" +@@ -612,7 +621,7 @@ + FORT_OPT=" $FORT_OPT -save -zero" + fi + if test "$MARCHDF_HDF" = "HDF"; then +- FORT_OPT="$FORT_OPT -DMARCHDF_HDF=$MARCHDF_HDF $HDF_INCLUDE" ++ FORT_OPT="$FORT_OPT -DMARCHDF=$MARCHDF_HDF" + fi + + FORTLOW="$FCOMP $FORT_OPT $PROFILE -O0 $I8FFLAGS -I$MARC_SOURCE/common \ +@@ -626,6 +635,29 @@ + # for compiling free form f90 files. high opt, integer(4) + FORTF90="$FCOMP -c -O3" + ++# determine DAMASK version ++if test -n "$DAMASK_USER"; then ++ DAMASKROOT=`dirname $DAMASK_USER`/../.. ++ read DAMASKVERSION < $DAMASKROOT/VERSION ++ DAMASKVERSION="'"$DAMASKVERSION"'" ++else ++ DAMASKVERSION="'N/A'" ++fi ++ ++# DAMASK compiler calls ++DFORTLOWMP="$FCOMP -c -O0 -qno-offload -implicitnone -stand f18 -standard-semantics -assume nostd_mod_proc_name -safe-cray-ptr $PROFILE -zero -mp1 -WB $I8FFLAGS -I$MARC_SOURCE/common \ ++ -fpp -ftz -diag-disable 5268 -warn declarations -warn general -warn usage -warn interfaces -warn ignore_loc -warn alignments -DMARC4DAMASK=2023.2 -DDAMASKVERSION=$DAMASKVERSION \ ++ -qopenmp -qopenmp-threadprivate=compat\ ++ $MUMPS_INCLUDE $I8DEFINES -DLinux -DLINUX -DLinux_intel $FDEFINES $DDM $SOLVERFLAGS -I$KDTREE2_MOD -I$MARC_MOD" ++DFORTRANMP="$FCOMP -c -O1 -qno-offload -implicitnone -stand f18 -standard-semantics -assume nostd_mod_proc_name -safe-cray-ptr $PROFILE -zero -mp1 -WB $I8FFLAGS -I$MARC_SOURCE/common \ ++ -fpp -ftz -diag-disable 5268 -warn declarations -warn general -warn usage -warn interfaces -warn ignore_loc -warn alignments -DMARC4DAMASK=2023.2 -DDAMASKVERSION=$DAMASKVERSION \ ++ -qopenmp -qopenmp-threadprivate=compat\ ++ $MUMPS_INCLUDE $I8DEFINES -DLinux -DLINUX -DLinux_intel $FDEFINES $DDM $SOLVERFLAGS -I$KDTREE2_MOD -I$MARC_MOD" ++DFORTHIGHMP="$FCOMP -c -O3 -qno-offload -implicitnone -stand f18 -standard-semantics -assume nostd_mod_proc_name -safe-cray-ptr $PROFILE -zero -mp1 -WB $I8FFLAGS -I$MARC_SOURCE/common \ ++ -fpp -ftz -diag-disable 5268 -warn declarations -warn general -warn usage -warn interfaces -warn ignore_loc -warn alignments -DMARC4DAMASK=2023.2 -DDAMASKVERSION=$DAMASKVERSION \ ++ -qopenmp -qopenmp-threadprivate=compat\ ++ $MUMPS_INCLUDE $I8DEFINES -DLinux -DLINUX -DLinux_intel $FDEFINES $DDM $SOLVERFLAGS -I$KDTREE2_MOD -I$MARC_MOD" ++ + if test "$MARCDEBUG" = "ON" + then + FORTLOW="$FCOMP $FORT_OPT $PROFILE $I8FFLAGS -I$MARC_SOURCE/common \ +@@ -783,7 +815,7 @@ + + SOLVERLIBS="${BCSSOLVERLIBS} ${VKISOLVERLIBS} ${CASISOLVERLIBS} ${MF2SOLVERLIBS} \ + -L$MARC_MKL \ +- $MARC_LIB/blas_src.a ${ACSI_LIB}/ACSI_MarcLib.a $KDTREE2_LIB/libkdtree2.a $MARC_LIB/libtetmeshinterface.a $MARC_LIB/libcaefatigueinterface.a -L$MARC_LIB -lmkl_blacs_intelmpi_ilp64 -lmkl_scalapack_ilp64 -lmkl_intel_ilp64 -lmkl_intel_thread -lmkl_core -liomp5 -ltetmesh -ltetadapt -lmeshgems -lmg-tetra -lmeshgems_stubs -lCATMshMesherCore -lCATMshCVMforCSM -lmg-hybrid -lmg-cadsurf -lmg-hexa $HDF_LIBS $SOLVER2LIBS $RTREE_LIB/librtree_lib.a $SFGEO_LIB/libgeo_lib.a" ++ $MARC_LIB/blas_src.a ${ACSI_LIB}/ACSI_MarcLib.a $KDTREE2_LIB/libkdtree2.a $MARC_LIB/libtetmeshinterface.a $MARC_LIB/libcaefatigueinterface.a -L$MARC_LIB -lmkl_blacs_intelmpi_ilp64 -lmkl_scalapack_ilp64 -lmkl_intel_ilp64 -lmkl_intel_thread -lmkl_core -liomp5 -ltetmesh -ltetadapt -lmeshgems -lmg-tetra -lmeshgems_stubs -lCATMshMesherCore -lCATMshCVMforCSM -lmg-hybrid -lmg-cadsurf -lmg-hexa $HDF5_LIB $SOLVER2LIBS $RTREE_LIB/librtree_lib.a $SFGEO_LIB/libgeo_lib.a" + + SOLVERLIBS_DLL=${SOLVERLIBS} + if test "$AEM_DLL" -eq 1 diff --git a/install/MarcMentat/2023.3/Marc_tools/run_damask_hmp.patch b/install/MarcMentat/2023.3/Marc_tools/run_damask_hmp.patch new file mode 100644 index 000000000..f05c41969 --- /dev/null +++ b/install/MarcMentat/2023.3/Marc_tools/run_damask_hmp.patch @@ -0,0 +1,477 @@ +--- ++++ +@@ -136,6 +136,11 @@ + # is created. For job running in the background, the log # + # file is always created. Default is "yes" # + ############################################################################## ++# remove all Mentat paths from LD_LIBRARY_PATH ++LD_LIBRARY_PATH=:$LD_LIBRARY_PATH: ++LD_LIBRARY_PATH=${LD_LIBRARY_PATH//+([!(:)])mentat2023.2+([!(:)])/:} ++LD_LIBRARY_PATH=${LD_LIBRARY_PATH//+([(:)])/:} ++LD_LIBRARY_PATH=${LD_LIBRARY_PATH#:}; LD_LIBRARY_PATH=${LD_LIBRARY_PATH%:} + # set DIR to the directory in which this script is + REALCOM="`/bin/ls -l $0 |awk '{ print $NF; }'`" + DIR=`dirname $REALCOM` +@@ -303,7 +308,23 @@ + + . "$DIR/getarch" + ++ ++# getting user subroutine file name ++found=0 ++for i in "$@"; do ++ if test $found = 1; then ++ DAMASK_USER=$i ++ found=0 ++ fi ++ case $i in ++ -u* | -U*) ++ found=1 ++ ;; ++ esac ++done ++# sourcing include_linux64 (needs DAMASK_USER to be set) + . $MARC_INCLUDE ++ + # + + # +@@ -406,7 +427,7 @@ + did= + vid= + user= +-usersubname= ++usernoext= + objs= + qid=background + cpu= +@@ -677,50 +698,19 @@ + esac + ;; + -u* | -U*) +- user=`dirname $value`/`$BASENAME $value .f` +- usersubname=$user +- basefile=`$BASENAME $value` +- if test ${basefile##*.} = f +- then +- user=`dirname $value`/`$BASENAME $value .f` +- usersubname=$user.f +- elif test ${basefile##*.} = F +- then +- user=`dirname $value`/`$BASENAME $value .F` +- usersubname=$user.F +- elif test ${basefile##*.} = f90 +- then +- user=`dirname $value`/`$BASENAME $value .f90` +- usersubname=$user.f90 +- elif test ${basefile##*.} = F90 +- then +- user=`dirname $value`/`$BASENAME $value .F90` +- usersubname=$user.F90 +- fi ++ user=$value + case $user in + \/*) + ;; + *) + user=`pwd`/$user +- usersubname=`pwd`/$usersubname + ;; + esac +- if test ! -f $usersubname +- then +- if test -f $usersubname.f +- then +- usersubname=$usersubname.f +- elif test -f $usersubname.F +- then +- usersubname=$usersubname.F +- elif test -f $usersubname.f90 +- then +- usersubname=$usersubname.f90 +- elif test -f $usersubname.F90 +- then +- usersubname=$usersubname.F90 +- fi +- fi ++ usernoext=$user ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .F` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .for` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f90` + ;; + -obj | -OBJ) + objs="$value" +@@ -1208,12 +1198,12 @@ + fi + fi + fi +- if test "$usersubname" ++ if test "$user" + then +- if test ! -f $usersubname ++ if test ! -f $user + then + error="$error +-user subroutine file $usersubname not accessible" ++user subroutine file $user not accessible" + fi + fi + if test "$objs" +@@ -1532,7 +1522,7 @@ + Marc shared lib : $progdll + Version type : $mode + Job ID : $DIRJID/$jid$extra_job_info +-User subroutine name : $usersubname ++User subroutine name : $user + User objects/libs : $objs + Restart file job ID : $rid + Substructure file ID : $sid +@@ -1565,7 +1555,7 @@ + Marc shared lib : $progdll + Version type : $mode + Job ID : $DIRJID/$jid$extra_job_info +-User subroutine name : $usersubname ++User subroutine name : $user + User objects/libs : $objs + Restart file job ID : $rid + Substructure file ID : $sid +@@ -1688,7 +1678,7 @@ + ;; + esac + fi +- $ECHO "User subroutine name ($usersubname)? $ECHOTXT" ++ $ECHO "User subroutine name ($user)? $ECHOTXT" + read value + if test "$value" + then +@@ -1697,50 +1687,19 @@ + user= + ;; + *) +- user=`dirname $value`/`$BASENAME $value .f` +- usersubname=$user +- basefile=`$BASENAME $value` +- if test ${basefile##*.} = f +- then +- user=`dirname $value`/`$BASENAME $value .f` +- usersubname=$user.f +- elif test ${basefile##*.} = F +- then +- user=`dirname $value`/`$BASENAME $value .F` +- usersubname=$user.F +- elif test ${basefile##*.} = f90 +- then +- user=`dirname $value`/`$BASENAME $value .f90` +- usersubname=$user.f90 +- elif test ${basefile##*.} = F90 +- then +- user=`dirname $value`/`$BASENAME $value .F90` +- usersubname=$user.F90 +- fi ++ user=$value + case $user in + \/*) + ;; + *) + user=`pwd`/$user +- usersubname=`pwd`/$usersubname + ;; + esac +- if test ! -f $usersubname +- then +- if test -f $usersubname.f +- then +- usersubname=$usersubname.f +- elif test -f $usersubname.F +- then +- usersubname=$usersubname.F +- elif test -f $usersubname.f90 +- then +- usersubname=$usersubname.f90 +- elif test -f $usersubname.F90 +- then +- usersubname=$usersubname.F90 +- fi +- fi ++ usernoext=$user ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .F` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .for` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f90` + ;; + esac + fi +@@ -2275,11 +2234,12 @@ + # + # user subroutine used + # ++# add DAMASK options for linking ++ DAMASK="-lstdc++" + + if test "$user" + then +-# program=$user.marc +- program=$DIRJOB/`$BASENAME $user .f`.marc ++ program=$usernoext.marc + case $program in + \/* | \.\/*) + bd= +@@ -2392,7 +2352,7 @@ + fi + if test "$user" + then +- execpath=$DIRJOB/`$BASENAME $user .f`.marc ++ execpath=$usernoext.marc + usersub=1 + fi + export execpath +@@ -3275,33 +3235,16 @@ + echo + if test "$user" + then +- userobj=$DIRJOB/`$BASENAME $user .f`.o +- basefile=`$BASENAME $usersubname` +- if test ${basefile##*.} = f +- then +- usersub=$DIRJOB/`$BASENAME $user .f`.F +- ln -sf "$user.f" "$usersub" +- else +- usersub=$usersubname +- fi +- ++ userobj=$usernoext.o + fi + cat > $jid.runmarcscript << END4 + if test "$user" + then +- if test ${basefile##*.} = f +- then +- ln -sf "$user.f" "$usersub" +- fi +- $FORTRAN $usersub -o $userobj || \ ++ $DFORTHIGHMP $user -o $userobj || \ + { +- echo "$0: compile failed for $usersubname" ++ echo "$0: compile failed for $user" + exit 1 + } + /bin/rm $program 2>/dev/null +- if test ${basefile##*.} = f +- then +- /bin/rm -f "$usersub" +- fi + fi + +@@ -3323,6 +3266,7 @@ + $TKLIBS \ + $MRCLIBS \ + $METISLIBS \ ++ $DAMASK \ + $SFLIB \ + $OPENSSL_LIB \ + $SYSLIBS \ +@@ -3336,6 +3280,9 @@ + prgsav=yes + fi + /bin/rm $userobj 2>/dev/null ++/bin/rm $DIRJOB/*.mod 2>/dev/null ++/bin/rm $DIRJOB/*.smod 2>/dev/null ++/bin/rm $DIRJOB/*_genmod.f90 2>/dev/null + + # + # run marc +@@ -3383,7 +3330,7 @@ + fi + else + if test $cpdll = yes; then +- filename=`basename $usersubname .f` ++ filename=$usernoext + /bin/cp $DIRJOB/$marcdll $DIRJOB/${filename}_$marcdll 2>/dev/null + fi + if test $rmdll = yes +@@ -3549,7 +3496,7 @@ + # first copy over the user sub if local directories + if test ${dirstatus[$counter]} = "local" + then +- $RCP $user.f $i:$DIR1/ ++ $RCP $user $i:$DIR1/ + fi + # do the compilation on the other machine + if test ${dirstatus[$counter]} = "shared" +@@ -3562,21 +3509,21 @@ + remoteuser=$DIR1/`$BASENAME $user` + $RSH $i /bin/rm $remoteprog 2> /dev/null + echo +- $RSH $i $DIR2/tools/comp_user $DIR2 $DIR1 $remoteuser $remoteprog ++ $RSH $i $DIR2/tools/comp_damask_hmp $DIR2 $DIR1 $remoteuser $remoteprog + # check if successful, the new executable should be there + line=`$RSH $i /bin/ls $remoteprog 2> /dev/null` + if test "$line" + then + echo compilation and linking successful on host $i + else +- echo "$0: compile failed for $usersubname on host $i" ++ echo "$0: compile failed for $user on host $i" + echo " $PRODUCT Exit number 3" + exit 1 + fi + # remove the user subroutine on remote machine + if test ${dirstatus[$counter]} = "local" + then +- $RSH $i /bin/rm $remoteuser.f 2> /dev/null ++ $RSH $i /bin/rm $remoteuser 2> /dev/null + fi + fi + fi +@@ -3586,27 +3533,15 @@ + if test "$userhost" + then + echo +- echo "Compiling and linking user subroutine $user.f on host `hostname`" +- fi +- userobj=$DIRJOB/`$BASENAME $user .f`.o +- basefile=`$BASENAME $usersubname` +- if test ${basefile##*.} = f +- then +- usersub=$DIRJOB/`$BASENAME $user .f`.F +- ln -sf "$user.f" "$usersub" +- else +- usersub=$usersubname ++ echo "Compiling and linking user subroutine $user on host `hostname`" + fi ++ userobj=$usernoext.o +- $FORTRAN $usersub -o $userobj || \ ++ $DFORTHIGHMP $user -o $userobj || \ + { +- echo "$0: compile failed for $usersubname" ++ echo "$0: compile failed for $user" + echo " $PRODUCT Exit number 3" + exit 1 + } + /bin/rm $program 2>/dev/null +- if test ${basefile##*.} = f +- then +- /bin/rm -f "$usersub" +- fi + fi # if test $user + +@@ -3628,6 +3563,7 @@ + $TKLIBS \ + $MRCLIBS \ + $METISLIBS \ ++ $DAMASK \ + $SFLIB \ + $OPENSSL_LIB \ + $SYSLIBS \ +@@ -3669,6 +3605,9 @@ + prgsav=yes + fi # if test $link + /bin/rm $userobj 2>/dev/null ++/bin/rm $DIRJOB/*.mod 2>/dev/null ++/bin/rm $DIRJOB/*.smod 2>/dev/null ++/bin/rm $DIRJOB/*_genmod.f90 2>/dev/null + + # + # run marc +@@ -3763,7 +3702,7 @@ + else + #dllrun >0 + if test $cpdll = yes; then +- filename=`basename $usersubname .f` ++ filename=$usernoext + /bin/cp $DIRJOB/$marcdll $DIRJOB/${filename}_$marcdll 2>/dev/null + fi + if test $rmdll = yes;then +@@ -3888,7 +3827,7 @@ + # first copy over the user sub if local directories + if test ${dirstatus[$counter]} = "local" + then +- $RCP $user.f $i:$DIR1/ ++ $RCP $user $i:$DIR1/ + fi + # do the compilation on the other machine + if test ${dirstatus[$counter]} = "shared" +@@ -3901,20 +3840,20 @@ + remoteuser=$DIR1/`$BASENAME $user` + $RSH $i /bin/rm $remoteprog 2> /dev/null + echo +- $RSH $i $DIR2/tools/comp_user $DIR2 $DIR1 $remoteuser $remoteprog ++ $RSH $i $DIR2/tools/comp_damask_hmp $DIR2 $DIR1 $remoteuser $remoteprog + # check if successful, the new executable should be there + line=`$RSH $i /bin/ls $remoteprog 2> /dev/null` + if test "$line" + then + echo compilation and linking successful on host $i + else +- echo "$0: compile failed for $usersubname on host $i" ++ echo "$0: compile failed for $user on host $i" + exit 1 + fi + # remove the user subroutine on remote machine + if test ${dirstatus[$counter]} = "local" + then +- $RSH $i /bin/rm $remoteuser.f 2> /dev/null ++ $RSH $i /bin/rm $remoteuser 2> /dev/null + fi + fi + fi +@@ -3924,26 +3863,14 @@ + if test "$userhost" + then + echo +- echo "Compiling and linking user subroutine $user.f on host `hostname`" +- fi +- userobj=$DIRJOB/`$BASENAME $user .f`.o +- basefile=`$BASENAME $usersubname` +- if test ${basefile##*.} = f +- then +- usersub=$DIRJOB/`$BASENAME $user .f`.F +- ln -sf "$user.f" "$usersub" +- else +- usersub=$usersubname ++ echo "Compiling and linking user subroutine $user on host `hostname`" + fi ++ userobj=$usernoext.o +- $FORTRAN $usersub -o $userobj || \ ++ $DFORTHIGHMP $user -o $userobj || \ + { +- echo "$0: compile failed for $usersubname" ++ echo "$0: compile failed for $user" + exit 1 + } + /bin/rm $program 2>/dev/null +- if test ${basefile##*.} = f +- then +- /bin/rm -f "$usersub" +- fi + fi # if test $user + +@@ -3965,6 +3892,7 @@ + $TKLIBS \ + $MRCLIBS \ + $METISLIBS \ ++ $DAMASK \ + $SFLIB \ + $OPENSSL_LIB \ + $SYSLIBS \ +@@ -4005,7 +3933,9 @@ + prgsav=yes + fi # if test $link + /bin/rm $userobj 2>/dev/null +- ++/bin/rm $DIRJOB/*.mod 2>/dev/null ++/bin/rm $DIRJOB/*.smod 2>/dev/null ++/bin/rm $DIRJOB/*_genmod.f90 2>/dev/null + # done if no job id given + if test -z "$jid" + then +@@ -4125,7 +4055,7 @@ + else + #dllrun >0 + if test $cpdll = yes; then +- filename=`basename $usersubname .f` ++ filename=$usernoext + /bin/cp $DIRJOB/$marcdll $DIRJOB/${filename}_$marcdll 2>/dev/null + fi + if test $rmdll = yes;then diff --git a/install/MarcMentat/2023.3/Marc_tools/run_damask_lmp.patch b/install/MarcMentat/2023.3/Marc_tools/run_damask_lmp.patch new file mode 100644 index 000000000..5ff268524 --- /dev/null +++ b/install/MarcMentat/2023.3/Marc_tools/run_damask_lmp.patch @@ -0,0 +1,477 @@ +--- ++++ +@@ -136,6 +136,11 @@ + # is created. For job running in the background, the log # + # file is always created. Default is "yes" # + ############################################################################## ++# remove all Mentat paths from LD_LIBRARY_PATH ++LD_LIBRARY_PATH=:$LD_LIBRARY_PATH: ++LD_LIBRARY_PATH=${LD_LIBRARY_PATH//+([!(:)])mentat2023.2+([!(:)])/:} ++LD_LIBRARY_PATH=${LD_LIBRARY_PATH//+([(:)])/:} ++LD_LIBRARY_PATH=${LD_LIBRARY_PATH#:}; LD_LIBRARY_PATH=${LD_LIBRARY_PATH%:} + # set DIR to the directory in which this script is + REALCOM="`/bin/ls -l $0 |awk '{ print $NF; }'`" + DIR=`dirname $REALCOM` +@@ -303,7 +308,23 @@ + + . "$DIR/getarch" + ++ ++# getting user subroutine file name ++found=0 ++for i in "$@"; do ++ if test $found = 1; then ++ DAMASK_USER=$i ++ found=0 ++ fi ++ case $i in ++ -u* | -U*) ++ found=1 ++ ;; ++ esac ++done ++# sourcing include_linux64 (needs DAMASK_USER to be set) + . $MARC_INCLUDE ++ + # + + # +@@ -406,7 +427,7 @@ + did= + vid= + user= +-usersubname= ++usernoext= + objs= + qid=background + cpu= +@@ -677,50 +698,19 @@ + esac + ;; + -u* | -U*) +- user=`dirname $value`/`$BASENAME $value .f` +- usersubname=$user +- basefile=`$BASENAME $value` +- if test ${basefile##*.} = f +- then +- user=`dirname $value`/`$BASENAME $value .f` +- usersubname=$user.f +- elif test ${basefile##*.} = F +- then +- user=`dirname $value`/`$BASENAME $value .F` +- usersubname=$user.F +- elif test ${basefile##*.} = f90 +- then +- user=`dirname $value`/`$BASENAME $value .f90` +- usersubname=$user.f90 +- elif test ${basefile##*.} = F90 +- then +- user=`dirname $value`/`$BASENAME $value .F90` +- usersubname=$user.F90 +- fi ++ user=$value + case $user in + \/*) + ;; + *) + user=`pwd`/$user +- usersubname=`pwd`/$usersubname + ;; + esac +- if test ! -f $usersubname +- then +- if test -f $usersubname.f +- then +- usersubname=$usersubname.f +- elif test -f $usersubname.F +- then +- usersubname=$usersubname.F +- elif test -f $usersubname.f90 +- then +- usersubname=$usersubname.f90 +- elif test -f $usersubname.F90 +- then +- usersubname=$usersubname.F90 +- fi +- fi ++ usernoext=$user ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .F` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .for` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f90` + ;; + -obj | -OBJ) + objs="$value" +@@ -1208,12 +1198,12 @@ + fi + fi + fi +- if test "$usersubname" ++ if test "$user" + then +- if test ! -f $usersubname ++ if test ! -f $user + then + error="$error +-user subroutine file $usersubname not accessible" ++user subroutine file $user not accessible" + fi + fi + if test "$objs" +@@ -1532,7 +1522,7 @@ + Marc shared lib : $progdll + Version type : $mode + Job ID : $DIRJID/$jid$extra_job_info +-User subroutine name : $usersubname ++User subroutine name : $user + User objects/libs : $objs + Restart file job ID : $rid + Substructure file ID : $sid +@@ -1565,7 +1555,7 @@ + Marc shared lib : $progdll + Version type : $mode + Job ID : $DIRJID/$jid$extra_job_info +-User subroutine name : $usersubname ++User subroutine name : $user + User objects/libs : $objs + Restart file job ID : $rid + Substructure file ID : $sid +@@ -1688,7 +1678,7 @@ + ;; + esac + fi +- $ECHO "User subroutine name ($usersubname)? $ECHOTXT" ++ $ECHO "User subroutine name ($user)? $ECHOTXT" + read value + if test "$value" + then +@@ -1697,50 +1687,19 @@ + user= + ;; + *) +- user=`dirname $value`/`$BASENAME $value .f` +- usersubname=$user +- basefile=`$BASENAME $value` +- if test ${basefile##*.} = f +- then +- user=`dirname $value`/`$BASENAME $value .f` +- usersubname=$user.f +- elif test ${basefile##*.} = F +- then +- user=`dirname $value`/`$BASENAME $value .F` +- usersubname=$user.F +- elif test ${basefile##*.} = f90 +- then +- user=`dirname $value`/`$BASENAME $value .f90` +- usersubname=$user.f90 +- elif test ${basefile##*.} = F90 +- then +- user=`dirname $value`/`$BASENAME $value .F90` +- usersubname=$user.F90 +- fi ++ user=$value + case $user in + \/*) + ;; + *) + user=`pwd`/$user +- usersubname=`pwd`/$usersubname + ;; + esac +- if test ! -f $usersubname +- then +- if test -f $usersubname.f +- then +- usersubname=$usersubname.f +- elif test -f $usersubname.F +- then +- usersubname=$usersubname.F +- elif test -f $usersubname.f90 +- then +- usersubname=$usersubname.f90 +- elif test -f $usersubname.F90 +- then +- usersubname=$usersubname.F90 +- fi +- fi ++ usernoext=$user ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .F` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .for` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f90` + ;; + esac + fi +@@ -2275,11 +2234,12 @@ + # + # user subroutine used + # ++# add DAMASK options for linking ++ DAMASK="-lstdc++" + + if test "$user" + then +-# program=$user.marc +- program=$DIRJOB/`$BASENAME $user .f`.marc ++ program=$usernoext.marc + case $program in + \/* | \.\/*) + bd= +@@ -2392,7 +2352,7 @@ + fi + if test "$user" + then +- execpath=$DIRJOB/`$BASENAME $user .f`.marc ++ execpath=$usernoext.marc + usersub=1 + fi + export execpath +@@ -3275,33 +3235,16 @@ + echo + if test "$user" + then +- userobj=$DIRJOB/`$BASENAME $user .f`.o +- basefile=`$BASENAME $usersubname` +- if test ${basefile##*.} = f +- then +- usersub=$DIRJOB/`$BASENAME $user .f`.F +- ln -sf "$user.f" "$usersub" +- else +- usersub=$usersubname +- fi +- ++ userobj=$usernoext.o + fi + cat > $jid.runmarcscript << END4 + if test "$user" + then +- if test ${basefile##*.} = f +- then +- ln -sf "$user.f" "$usersub" +- fi +- $FORTRAN $usersub -o $userobj || \ ++ $DFORTLOWMP $user -o $userobj || \ + { +- echo "$0: compile failed for $usersubname" ++ echo "$0: compile failed for $user" + exit 1 + } + /bin/rm $program 2>/dev/null +- if test ${basefile##*.} = f +- then +- /bin/rm -f "$usersub" +- fi + fi + +@@ -3323,6 +3266,7 @@ + $TKLIBS \ + $MRCLIBS \ + $METISLIBS \ ++ $DAMASK \ + $SFLIB \ + $OPENSSL_LIB \ + $SYSLIBS \ +@@ -3336,6 +3280,9 @@ + prgsav=yes + fi + /bin/rm $userobj 2>/dev/null ++/bin/rm $DIRJOB/*.mod 2>/dev/null ++/bin/rm $DIRJOB/*.smod 2>/dev/null ++/bin/rm $DIRJOB/*_genmod.f90 2>/dev/null + + # + # run marc +@@ -3383,7 +3330,7 @@ + fi + else + if test $cpdll = yes; then +- filename=`basename $usersubname .f` ++ filename=$usernoext + /bin/cp $DIRJOB/$marcdll $DIRJOB/${filename}_$marcdll 2>/dev/null + fi + if test $rmdll = yes +@@ -3549,7 +3496,7 @@ + # first copy over the user sub if local directories + if test ${dirstatus[$counter]} = "local" + then +- $RCP $user.f $i:$DIR1/ ++ $RCP $user $i:$DIR1/ + fi + # do the compilation on the other machine + if test ${dirstatus[$counter]} = "shared" +@@ -3562,21 +3509,21 @@ + remoteuser=$DIR1/`$BASENAME $user` + $RSH $i /bin/rm $remoteprog 2> /dev/null + echo +- $RSH $i $DIR2/tools/comp_user $DIR2 $DIR1 $remoteuser $remoteprog ++ $RSH $i $DIR2/tools/comp_damask_lmp $DIR2 $DIR1 $remoteuser $remoteprog + # check if successful, the new executable should be there + line=`$RSH $i /bin/ls $remoteprog 2> /dev/null` + if test "$line" + then + echo compilation and linking successful on host $i + else +- echo "$0: compile failed for $usersubname on host $i" ++ echo "$0: compile failed for $user on host $i" + echo " $PRODUCT Exit number 3" + exit 1 + fi + # remove the user subroutine on remote machine + if test ${dirstatus[$counter]} = "local" + then +- $RSH $i /bin/rm $remoteuser.f 2> /dev/null ++ $RSH $i /bin/rm $remoteuser 2> /dev/null + fi + fi + fi +@@ -3586,27 +3533,15 @@ + if test "$userhost" + then + echo +- echo "Compiling and linking user subroutine $user.f on host `hostname`" +- fi +- userobj=$DIRJOB/`$BASENAME $user .f`.o +- basefile=`$BASENAME $usersubname` +- if test ${basefile##*.} = f +- then +- usersub=$DIRJOB/`$BASENAME $user .f`.F +- ln -sf "$user.f" "$usersub" +- else +- usersub=$usersubname ++ echo "Compiling and linking user subroutine $user on host `hostname`" + fi ++ userobj=$usernoext.o +- $FORTRAN $usersub -o $userobj || \ ++ $DFORTLOWMP $user -o $userobj || \ + { +- echo "$0: compile failed for $usersubname" ++ echo "$0: compile failed for $user" + echo " $PRODUCT Exit number 3" + exit 1 + } + /bin/rm $program 2>/dev/null +- if test ${basefile##*.} = f +- then +- /bin/rm -f "$usersub" +- fi + fi # if test $user + +@@ -3628,6 +3563,7 @@ + $TKLIBS \ + $MRCLIBS \ + $METISLIBS \ ++ $DAMASK \ + $SFLIB \ + $OPENSSL_LIB \ + $SYSLIBS \ +@@ -3669,6 +3605,9 @@ + prgsav=yes + fi # if test $link + /bin/rm $userobj 2>/dev/null ++/bin/rm $DIRJOB/*.mod 2>/dev/null ++/bin/rm $DIRJOB/*.smod 2>/dev/null ++/bin/rm $DIRJOB/*_genmod.f90 2>/dev/null + + # + # run marc +@@ -3763,7 +3702,7 @@ + else + #dllrun >0 + if test $cpdll = yes; then +- filename=`basename $usersubname .f` ++ filename=$usernoext + /bin/cp $DIRJOB/$marcdll $DIRJOB/${filename}_$marcdll 2>/dev/null + fi + if test $rmdll = yes;then +@@ -3888,7 +3827,7 @@ + # first copy over the user sub if local directories + if test ${dirstatus[$counter]} = "local" + then +- $RCP $user.f $i:$DIR1/ ++ $RCP $user $i:$DIR1/ + fi + # do the compilation on the other machine + if test ${dirstatus[$counter]} = "shared" +@@ -3901,20 +3840,20 @@ + remoteuser=$DIR1/`$BASENAME $user` + $RSH $i /bin/rm $remoteprog 2> /dev/null + echo +- $RSH $i $DIR2/tools/comp_user $DIR2 $DIR1 $remoteuser $remoteprog ++ $RSH $i $DIR2/tools/comp_damask_lmp $DIR2 $DIR1 $remoteuser $remoteprog + # check if successful, the new executable should be there + line=`$RSH $i /bin/ls $remoteprog 2> /dev/null` + if test "$line" + then + echo compilation and linking successful on host $i + else +- echo "$0: compile failed for $usersubname on host $i" ++ echo "$0: compile failed for $user on host $i" + exit 1 + fi + # remove the user subroutine on remote machine + if test ${dirstatus[$counter]} = "local" + then +- $RSH $i /bin/rm $remoteuser.f 2> /dev/null ++ $RSH $i /bin/rm $remoteuser 2> /dev/null + fi + fi + fi +@@ -3924,26 +3863,14 @@ + if test "$userhost" + then + echo +- echo "Compiling and linking user subroutine $user.f on host `hostname`" +- fi +- userobj=$DIRJOB/`$BASENAME $user .f`.o +- basefile=`$BASENAME $usersubname` +- if test ${basefile##*.} = f +- then +- usersub=$DIRJOB/`$BASENAME $user .f`.F +- ln -sf "$user.f" "$usersub" +- else +- usersub=$usersubname ++ echo "Compiling and linking user subroutine $user on host `hostname`" + fi ++ userobj=$usernoext.o +- $FORTRAN $usersub -o $userobj || \ ++ $DFORTLOWMP $user -o $userobj || \ + { +- echo "$0: compile failed for $usersubname" ++ echo "$0: compile failed for $user" + exit 1 + } + /bin/rm $program 2>/dev/null +- if test ${basefile##*.} = f +- then +- /bin/rm -f "$usersub" +- fi + fi # if test $user + +@@ -3965,6 +3892,7 @@ + $TKLIBS \ + $MRCLIBS \ + $METISLIBS \ ++ $DAMASK \ + $SFLIB \ + $OPENSSL_LIB \ + $SYSLIBS \ +@@ -4005,7 +3933,9 @@ + prgsav=yes + fi # if test $link + /bin/rm $userobj 2>/dev/null +- ++/bin/rm $DIRJOB/*.mod 2>/dev/null ++/bin/rm $DIRJOB/*.smod 2>/dev/null ++/bin/rm $DIRJOB/*_genmod.f90 2>/dev/null + # done if no job id given + if test -z "$jid" + then +@@ -4125,7 +4055,7 @@ + else + #dllrun >0 + if test $cpdll = yes; then +- filename=`basename $usersubname .f` ++ filename=$usernoext + /bin/cp $DIRJOB/$marcdll $DIRJOB/${filename}_$marcdll 2>/dev/null + fi + if test $rmdll = yes;then diff --git a/install/MarcMentat/2023.3/Marc_tools/run_damask_mp.patch b/install/MarcMentat/2023.3/Marc_tools/run_damask_mp.patch new file mode 100644 index 000000000..579952b1f --- /dev/null +++ b/install/MarcMentat/2023.3/Marc_tools/run_damask_mp.patch @@ -0,0 +1,477 @@ +--- ++++ +@@ -136,6 +136,11 @@ + # is created. For job running in the background, the log # + # file is always created. Default is "yes" # + ############################################################################## ++# remove all Mentat paths from LD_LIBRARY_PATH ++LD_LIBRARY_PATH=:$LD_LIBRARY_PATH: ++LD_LIBRARY_PATH=${LD_LIBRARY_PATH//+([!(:)])mentat2023.2+([!(:)])/:} ++LD_LIBRARY_PATH=${LD_LIBRARY_PATH//+([(:)])/:} ++LD_LIBRARY_PATH=${LD_LIBRARY_PATH#:}; LD_LIBRARY_PATH=${LD_LIBRARY_PATH%:} + # set DIR to the directory in which this script is + REALCOM="`/bin/ls -l $0 |awk '{ print $NF; }'`" + DIR=`dirname $REALCOM` +@@ -303,7 +308,23 @@ + + . "$DIR/getarch" + ++ ++# getting user subroutine file name ++found=0 ++for i in "$@"; do ++ if test $found = 1; then ++ DAMASK_USER=$i ++ found=0 ++ fi ++ case $i in ++ -u* | -U*) ++ found=1 ++ ;; ++ esac ++done ++# sourcing include_linux64 (needs DAMASK_USER to be set) + . $MARC_INCLUDE ++ + # + + # +@@ -406,7 +427,7 @@ + did= + vid= + user= +-usersubname= ++usernoext= + objs= + qid=background + cpu= +@@ -677,50 +698,19 @@ + esac + ;; + -u* | -U*) +- user=`dirname $value`/`$BASENAME $value .f` +- usersubname=$user +- basefile=`$BASENAME $value` +- if test ${basefile##*.} = f +- then +- user=`dirname $value`/`$BASENAME $value .f` +- usersubname=$user.f +- elif test ${basefile##*.} = F +- then +- user=`dirname $value`/`$BASENAME $value .F` +- usersubname=$user.F +- elif test ${basefile##*.} = f90 +- then +- user=`dirname $value`/`$BASENAME $value .f90` +- usersubname=$user.f90 +- elif test ${basefile##*.} = F90 +- then +- user=`dirname $value`/`$BASENAME $value .F90` +- usersubname=$user.F90 +- fi ++ user=$value + case $user in + \/*) + ;; + *) + user=`pwd`/$user +- usersubname=`pwd`/$usersubname + ;; + esac +- if test ! -f $usersubname +- then +- if test -f $usersubname.f +- then +- usersubname=$usersubname.f +- elif test -f $usersubname.F +- then +- usersubname=$usersubname.F +- elif test -f $usersubname.f90 +- then +- usersubname=$usersubname.f90 +- elif test -f $usersubname.F90 +- then +- usersubname=$usersubname.F90 +- fi +- fi ++ usernoext=$user ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .F` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .for` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f90` + ;; + -obj | -OBJ) + objs="$value" +@@ -1208,12 +1198,12 @@ + fi + fi + fi +- if test "$usersubname" ++ if test "$user" + then +- if test ! -f $usersubname ++ if test ! -f $user + then + error="$error +-user subroutine file $usersubname not accessible" ++user subroutine file $user not accessible" + fi + fi + if test "$objs" +@@ -1532,7 +1522,7 @@ + Marc shared lib : $progdll + Version type : $mode + Job ID : $DIRJID/$jid$extra_job_info +-User subroutine name : $usersubname ++User subroutine name : $user + User objects/libs : $objs + Restart file job ID : $rid + Substructure file ID : $sid +@@ -1565,7 +1555,7 @@ + Marc shared lib : $progdll + Version type : $mode + Job ID : $DIRJID/$jid$extra_job_info +-User subroutine name : $usersubname ++User subroutine name : $user + User objects/libs : $objs + Restart file job ID : $rid + Substructure file ID : $sid +@@ -1688,7 +1678,7 @@ + ;; + esac + fi +- $ECHO "User subroutine name ($usersubname)? $ECHOTXT" ++ $ECHO "User subroutine name ($user)? $ECHOTXT" + read value + if test "$value" + then +@@ -1697,50 +1687,19 @@ + user= + ;; + *) +- user=`dirname $value`/`$BASENAME $value .f` +- usersubname=$user +- basefile=`$BASENAME $value` +- if test ${basefile##*.} = f +- then +- user=`dirname $value`/`$BASENAME $value .f` +- usersubname=$user.f +- elif test ${basefile##*.} = F +- then +- user=`dirname $value`/`$BASENAME $value .F` +- usersubname=$user.F +- elif test ${basefile##*.} = f90 +- then +- user=`dirname $value`/`$BASENAME $value .f90` +- usersubname=$user.f90 +- elif test ${basefile##*.} = F90 +- then +- user=`dirname $value`/`$BASENAME $value .F90` +- usersubname=$user.F90 +- fi ++ user=$value + case $user in + \/*) + ;; + *) + user=`pwd`/$user +- usersubname=`pwd`/$usersubname + ;; + esac +- if test ! -f $usersubname +- then +- if test -f $usersubname.f +- then +- usersubname=$usersubname.f +- elif test -f $usersubname.F +- then +- usersubname=$usersubname.F +- elif test -f $usersubname.f90 +- then +- usersubname=$usersubname.f90 +- elif test -f $usersubname.F90 +- then +- usersubname=$usersubname.F90 +- fi +- fi ++ usernoext=$user ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .F` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .for` ++ usernoext=`dirname $usernoext`/`$BASENAME $usernoext .f90` + ;; + esac + fi +@@ -2275,11 +2234,12 @@ + # + # user subroutine used + # ++# add DAMASK options for linking ++ DAMASK="-lstdc++" + + if test "$user" + then +-# program=$user.marc +- program=$DIRJOB/`$BASENAME $user .f`.marc ++ program=$usernoext.marc + case $program in + \/* | \.\/*) + bd= +@@ -2392,7 +2352,7 @@ + fi + if test "$user" + then +- execpath=$DIRJOB/`$BASENAME $user .f`.marc ++ execpath=$usernoext.marc + usersub=1 + fi + export execpath +@@ -3275,33 +3235,16 @@ + echo + if test "$user" + then +- userobj=$DIRJOB/`$BASENAME $user .f`.o +- basefile=`$BASENAME $usersubname` +- if test ${basefile##*.} = f +- then +- usersub=$DIRJOB/`$BASENAME $user .f`.F +- ln -sf "$user.f" "$usersub" +- else +- usersub=$usersubname +- fi +- ++ userobj=$usernoext.o + fi + cat > $jid.runmarcscript << END4 + if test "$user" + then +- if test ${basefile##*.} = f +- then +- ln -sf "$user.f" "$usersub" +- fi +- $FORTRAN $usersub -o $userobj || \ ++ $DFORTRANMP $user -o $userobj || \ + { +- echo "$0: compile failed for $usersubname" ++ echo "$0: compile failed for $user" + exit 1 + } + /bin/rm $program 2>/dev/null +- if test ${basefile##*.} = f +- then +- /bin/rm -f "$usersub" +- fi + fi + +@@ -3323,6 +3266,7 @@ + $TKLIBS \ + $MRCLIBS \ + $METISLIBS \ ++ $DAMASK \ + $SFLIB \ + $OPENSSL_LIB \ + $SYSLIBS \ +@@ -3336,6 +3280,9 @@ + prgsav=yes + fi + /bin/rm $userobj 2>/dev/null ++/bin/rm $DIRJOB/*.mod 2>/dev/null ++/bin/rm $DIRJOB/*.smod 2>/dev/null ++/bin/rm $DIRJOB/*_genmod.f90 2>/dev/null + + # + # run marc +@@ -3383,7 +3330,7 @@ + fi + else + if test $cpdll = yes; then +- filename=`basename $usersubname .f` ++ filename=$usernoext + /bin/cp $DIRJOB/$marcdll $DIRJOB/${filename}_$marcdll 2>/dev/null + fi + if test $rmdll = yes +@@ -3549,7 +3496,7 @@ + # first copy over the user sub if local directories + if test ${dirstatus[$counter]} = "local" + then +- $RCP $user.f $i:$DIR1/ ++ $RCP $user $i:$DIR1/ + fi + # do the compilation on the other machine + if test ${dirstatus[$counter]} = "shared" +@@ -3562,21 +3509,21 @@ + remoteuser=$DIR1/`$BASENAME $user` + $RSH $i /bin/rm $remoteprog 2> /dev/null + echo +- $RSH $i $DIR2/tools/comp_user $DIR2 $DIR1 $remoteuser $remoteprog ++ $RSH $i $DIR2/tools/comp_damask_mp $DIR2 $DIR1 $remoteuser $remoteprog + # check if successful, the new executable should be there + line=`$RSH $i /bin/ls $remoteprog 2> /dev/null` + if test "$line" + then + echo compilation and linking successful on host $i + else +- echo "$0: compile failed for $usersubname on host $i" ++ echo "$0: compile failed for $user on host $i" + echo " $PRODUCT Exit number 3" + exit 1 + fi + # remove the user subroutine on remote machine + if test ${dirstatus[$counter]} = "local" + then +- $RSH $i /bin/rm $remoteuser.f 2> /dev/null ++ $RSH $i /bin/rm $remoteuser 2> /dev/null + fi + fi + fi +@@ -3586,27 +3533,15 @@ + if test "$userhost" + then + echo +- echo "Compiling and linking user subroutine $user.f on host `hostname`" +- fi +- userobj=$DIRJOB/`$BASENAME $user .f`.o +- basefile=`$BASENAME $usersubname` +- if test ${basefile##*.} = f +- then +- usersub=$DIRJOB/`$BASENAME $user .f`.F +- ln -sf "$user.f" "$usersub" +- else +- usersub=$usersubname ++ echo "Compiling and linking user subroutine $user on host `hostname`" + fi ++ userobj=$usernoext.o +- $FORTRAN $usersub -o $userobj || \ ++ $DFORTRANMP $user -o $userobj || \ + { +- echo "$0: compile failed for $usersubname" ++ echo "$0: compile failed for $user" + echo " $PRODUCT Exit number 3" + exit 1 + } + /bin/rm $program 2>/dev/null +- if test ${basefile##*.} = f +- then +- /bin/rm -f "$usersub" +- fi + fi # if test $user + +@@ -3628,6 +3563,7 @@ + $TKLIBS \ + $MRCLIBS \ + $METISLIBS \ ++ $DAMASK \ + $SFLIB \ + $OPENSSL_LIB \ + $SYSLIBS \ +@@ -3669,6 +3605,9 @@ + prgsav=yes + fi # if test $link + /bin/rm $userobj 2>/dev/null ++/bin/rm $DIRJOB/*.mod 2>/dev/null ++/bin/rm $DIRJOB/*.smod 2>/dev/null ++/bin/rm $DIRJOB/*_genmod.f90 2>/dev/null + + # + # run marc +@@ -3763,7 +3702,7 @@ + else + #dllrun >0 + if test $cpdll = yes; then +- filename=`basename $usersubname .f` ++ filename=$usernoext + /bin/cp $DIRJOB/$marcdll $DIRJOB/${filename}_$marcdll 2>/dev/null + fi + if test $rmdll = yes;then +@@ -3888,7 +3827,7 @@ + # first copy over the user sub if local directories + if test ${dirstatus[$counter]} = "local" + then +- $RCP $user.f $i:$DIR1/ ++ $RCP $user $i:$DIR1/ + fi + # do the compilation on the other machine + if test ${dirstatus[$counter]} = "shared" +@@ -3901,20 +3840,20 @@ + remoteuser=$DIR1/`$BASENAME $user` + $RSH $i /bin/rm $remoteprog 2> /dev/null + echo +- $RSH $i $DIR2/tools/comp_user $DIR2 $DIR1 $remoteuser $remoteprog ++ $RSH $i $DIR2/tools/comp_damask_mp $DIR2 $DIR1 $remoteuser $remoteprog + # check if successful, the new executable should be there + line=`$RSH $i /bin/ls $remoteprog 2> /dev/null` + if test "$line" + then + echo compilation and linking successful on host $i + else +- echo "$0: compile failed for $usersubname on host $i" ++ echo "$0: compile failed for $user on host $i" + exit 1 + fi + # remove the user subroutine on remote machine + if test ${dirstatus[$counter]} = "local" + then +- $RSH $i /bin/rm $remoteuser.f 2> /dev/null ++ $RSH $i /bin/rm $remoteuser 2> /dev/null + fi + fi + fi +@@ -3924,26 +3863,14 @@ + if test "$userhost" + then + echo +- echo "Compiling and linking user subroutine $user.f on host `hostname`" +- fi +- userobj=$DIRJOB/`$BASENAME $user .f`.o +- basefile=`$BASENAME $usersubname` +- if test ${basefile##*.} = f +- then +- usersub=$DIRJOB/`$BASENAME $user .f`.F +- ln -sf "$user.f" "$usersub" +- else +- usersub=$usersubname ++ echo "Compiling and linking user subroutine $user on host `hostname`" + fi ++ userobj=$usernoext.o +- $FORTRAN $usersub -o $userobj || \ ++ $DFORTRANMP $user -o $userobj || \ + { +- echo "$0: compile failed for $usersubname" ++ echo "$0: compile failed for $user" + exit 1 + } + /bin/rm $program 2>/dev/null +- if test ${basefile##*.} = f +- then +- /bin/rm -f "$usersub" +- fi + fi # if test $user + +@@ -3965,6 +3892,7 @@ + $TKLIBS \ + $MRCLIBS \ + $METISLIBS \ ++ $DAMASK \ + $SFLIB \ + $OPENSSL_LIB \ + $SYSLIBS \ +@@ -4005,7 +3933,9 @@ + prgsav=yes + fi # if test $link + /bin/rm $userobj 2>/dev/null +- ++/bin/rm $DIRJOB/*.mod 2>/dev/null ++/bin/rm $DIRJOB/*.smod 2>/dev/null ++/bin/rm $DIRJOB/*_genmod.f90 2>/dev/null + # done if no job id given + if test -z "$jid" + then +@@ -4125,7 +4055,7 @@ + else + #dllrun >0 + if test $cpdll = yes; then +- filename=`basename $usersubname .f` ++ filename=$usernoext + /bin/cp $DIRJOB/$marcdll $DIRJOB/${filename}_$marcdll 2>/dev/null + fi + if test $rmdll = yes;then diff --git a/install/MarcMentat/2023.3/Mentat_bin/edit_window.patch b/install/MarcMentat/2023.3/Mentat_bin/edit_window.patch new file mode 100644 index 000000000..915af9bf6 --- /dev/null +++ b/install/MarcMentat/2023.3/Mentat_bin/edit_window.patch @@ -0,0 +1,24 @@ +--- ++++ +@@ -1,18 +1,5 @@ + #!/bin/sh +-# This script opens a window running an editor. The default window is an +-# xterm, and the default editor is vi. These may be customized. ++# This script opens a window running an editor. ++# The command to invoke the editor is specified during DAMASK installation + +-dir= +-for d in /usr/bin /usr/bin/X11; do +- if test -x "$d/xterm"; then +- dir="$d" +- break +- fi +-done +- +-if test -z "$dir"; then +- echo "$0: Could not find xterm" +- exit 1 +-fi +- +-"$dir/xterm" -T "vi $*" -n "vi $*" -e vi $* ++%EDITOR% $* diff --git a/install/MarcMentat/2023.3/Mentat_bin/kill4.patch b/install/MarcMentat/2023.3/Mentat_bin/kill4.patch new file mode 100644 index 000000000..e69de29bb diff --git a/install/MarcMentat/2023.3/Mentat_bin/kill5.patch b/install/MarcMentat/2023.3/Mentat_bin/kill5.patch new file mode 100644 index 000000000..e69de29bb diff --git a/install/MarcMentat/2023.3/Mentat_bin/kill6.patch b/install/MarcMentat/2023.3/Mentat_bin/kill6.patch new file mode 100644 index 000000000..e69de29bb diff --git a/install/MarcMentat/2023.3/Mentat_bin/submit4.patch b/install/MarcMentat/2023.3/Mentat_bin/submit4.patch new file mode 100644 index 000000000..569ffaef0 --- /dev/null +++ b/install/MarcMentat/2023.3/Mentat_bin/submit4.patch @@ -0,0 +1,25 @@ +--- ++++ +@@ -91,6 +91,7 @@ + srcfile="-u $srcfile -save y" + ;; + runsaved) ++ srcfile=${srcfile%.*}".marc" + srcfile="-prog $srcfile" + ;; + esac +@@ -189,12 +190,12 @@ + unset PYTHONPATH + + if [ "$doe_first" = "-" ]; then # submit of regular Marc job +- "${DIR}/tools/run_marc" $slv -j $job -v n -b y $nprocds $nprocd \ ++ "${DIR}/tools/run_damask_hmp" $slv -j $job -v n -b y $nprocds $nprocd \ + $srcfile $restart $postfile $viewfactorsfile $hostfile \ + $compat $copy_datfile $copy_postfile $scr_dir $dcoup \ + $assem_recov_nthread $nthread $nsolver $mode $gpu > /dev/null 2>&1 + else # submit of a DoE Marc job +- "${DIR}/tools/run_marc" $slv -j $job -v n -b n $nprocds $nprocd \ ++ "${DIR}/tools/run_damask_hmp" $slv -j $job -v n -b n $nprocds $nprocd \ + $srcfile $restart $postfile $viewfactorsfile $hostfile \ + $compat $copy_datfile $copy_postfile $scr_dir $dcoup \ + $assem_recov_nthread $nthread $nsolver $mode $gpu diff --git a/install/MarcMentat/2023.3/Mentat_bin/submit5.patch b/install/MarcMentat/2023.3/Mentat_bin/submit5.patch new file mode 100644 index 000000000..542ad3cdc --- /dev/null +++ b/install/MarcMentat/2023.3/Mentat_bin/submit5.patch @@ -0,0 +1,25 @@ +--- ++++ +@@ -91,6 +91,7 @@ + srcfile="-u $srcfile -save y" + ;; + runsaved) ++ srcfile=${srcfile%.*}".marc" + srcfile="-prog $srcfile" + ;; + esac +@@ -189,12 +190,12 @@ + unset PYTHONPATH + + if [ "$doe_first" = "-" ]; then # submit of regular Marc job +- "${DIR}/tools/run_marc" $slv -j $job -v n -b y $nprocds $nprocd \ ++ "${DIR}/tools/run_damask_mp" $slv -j $job -v n -b y $nprocds $nprocd \ + $srcfile $restart $postfile $viewfactorsfile $hostfile \ + $compat $copy_datfile $copy_postfile $scr_dir $dcoup \ + $assem_recov_nthread $nthread $nsolver $mode $gpu > /dev/null 2>&1 + else # submit of a DoE Marc job +- "${DIR}/tools/run_marc" $slv -j $job -v n -b n $nprocds $nprocd \ ++ "${DIR}/tools/run_damask_mp" $slv -j $job -v n -b n $nprocds $nprocd \ + $srcfile $restart $postfile $viewfactorsfile $hostfile \ + $compat $copy_datfile $copy_postfile $scr_dir $dcoup \ + $assem_recov_nthread $nthread $nsolver $mode $gpu diff --git a/install/MarcMentat/2023.3/Mentat_bin/submit6.patch b/install/MarcMentat/2023.3/Mentat_bin/submit6.patch new file mode 100644 index 000000000..991b8add8 --- /dev/null +++ b/install/MarcMentat/2023.3/Mentat_bin/submit6.patch @@ -0,0 +1,25 @@ +--- ++++ +@@ -91,6 +91,7 @@ + srcfile="-u $srcfile -save y" + ;; + runsaved) ++ srcfile=${srcfile%.*}".marc" + srcfile="-prog $srcfile" + ;; + esac +@@ -189,12 +190,12 @@ + unset PYTHONPATH + + if [ "$doe_first" = "-" ]; then # submit of regular Marc job +- "${DIR}/tools/run_marc" $slv -j $job -v n -b y $nprocds $nprocd \ ++ "${DIR}/tools/run_damask_lmp" $slv -j $job -v n -b y $nprocds $nprocd \ + $srcfile $restart $postfile $viewfactorsfile $hostfile \ + $compat $copy_datfile $copy_postfile $scr_dir $dcoup \ + $assem_recov_nthread $nthread $nsolver $mode $gpu > /dev/null 2>&1 + else # submit of a DoE Marc job +- "${DIR}/tools/run_marc" $slv -j $job -v n -b n $nprocds $nprocd \ ++ "${DIR}/tools/run_damask_lmp" $slv -j $job -v n -b n $nprocds $nprocd \ + $srcfile $restart $postfile $viewfactorsfile $hostfile \ + $compat $copy_datfile $copy_postfile $scr_dir $dcoup \ + $assem_recov_nthread $nthread $nsolver $mode $gpu diff --git a/install/MarcMentat/2023.3/Mentat_menus/job_run.ms.patch b/install/MarcMentat/2023.3/Mentat_menus/job_run.ms.patch new file mode 100644 index 000000000..92ffcf8bd --- /dev/null +++ b/install/MarcMentat/2023.3/Mentat_menus/job_run.ms.patch @@ -0,0 +1,158 @@ +--- ++++ +@@ -362,11 +362,18 @@ + } + button { + position +25 = +- size 25 4 ++ size 18 4 + text "ADVANCED JOB SUBMISSION" + help "job_run#Job Submission And Control" + popmenu job_submit_adv_pm + } ++ button { ++ position +18 = ++ size 7 4 ++ text "DAMASK" ++ help "damask_run#Job Submission And Control" ++ popmenu damask ++ } + button { + position 0 +4 + size 16 4 +@@ -1307,6 +1314,135 @@ + } + + ++#-------------------------------------------------------------------------------------------------- ++popmenu damask { ++ ++#ifdef QT_MENTAT ++ text "DAMASK.MPIE.DE" ++#endif ++ ++ group { ++#ifndef QT_MENTAT ++ label { ++ position 0 0 ++ size 50 4 ++ text "DAMASK.MPIE.DE" ++ } ++#endif ++ ++ label { ++ position 1 6 ++ size 13 6 ++ text "Optimzation" ++ border_width 1 ++ border_color black ++ } ++ ++ label { ++ position +13 = ++ size 20 6 ++ text "write Input" ++ border_width 1 ++ border_color black ++ } ++ ++ label { ++ position +18 = ++ size 30 6 ++ text "do not write Inp." ++ border_width 1 ++ border_color black ++ } ++ ++ label { ++ position -32 +6 ++ size 12 6 ++ text "O3 / OpenMP" ++ border_width 1 ++ border_color black ++ } ++ ++ popdown { ++ position +12 = ++ size 20 6 ++ text "Submit" ++ command "*submit_job 4 *monitor_job" ++ } ++ ++ popdown { ++ position +20 = ++ size 20 6 ++ text "Execute" ++ command "*execute_job 4 *monitor_job" ++ } ++ ++ label { ++ position -32 +6 ++ size 12 6 ++ text "O1 / OpenMP" ++ border_width 1 ++ border_color black ++ } ++ ++ popdown { ++ position +12 = ++ size 20 6 ++ text "Submit" ++ command "*submit_job 5 *monitor_job" ++ } ++ ++ popdown { ++ position +20 = ++ size 20 6 ++ text "Execute" ++ command "*execute_job 5 *monitor_job" ++ } ++ ++ label { ++ position -32 +6 ++ size 12 6 ++ text "O0 / OpenMP" ++ border_width 1 ++ border_color black ++ } ++ ++ popdown { ++ position +12 = ++ size 20 6 ++ text "Submit" ++ command "*submit_job 6 *monitor_job" ++ } ++ ++ popdown { ++ position +20 = ++ size 20 6 ++ text "Execute" ++ command "*execute_job 6 *monitor_job" ++ } ++ ++ popdown { ++ position 19 +8 ++ size 12 8 ++ text "CANCEL" ++ } ++} ++ ++ window { ++ parent mentat ++ origin 38 8 ++#ifdef DCOM ++ size 50 100 ++#else ++ size 50 94 ++#endif ++ background_color body ++ border_width 1 ++ border_color border ++ buffering single ++ } ++ mode permanent ++} ++ + #-------------------------------------------------------------------------------------------------- + popmenu job_exit_msg_pm { + diff --git a/src/Marc/include/concom2023.3 b/src/Marc/include/concom2023.3 new file mode 100644 index 000000000..df42413cf --- /dev/null +++ b/src/Marc/include/concom2023.3 @@ -0,0 +1,473 @@ +! common block definition file taken from respective MSC.Marc release and reformated to free format +!*********************************************************************** +! +! File: concom.cmn +! +! MSC.Marc include file +! +integer & + iacous, iasmbl, iautth, ibear, icompl, iconj, icreep, ideva, idyn, idynt,& + ielas, ielcma, ielect, iform, ifour, iharm, ihcps, iheat, iheatt, ihresp,& + ijoule, ilem, ilnmom, iloren, inc, incext, incsub, ipass, iplres, ipois,& + ipoist, irpflo, ismall, ismalt, isoil, ispect, ispnow, istore, iswep, ithcrp,& + itherm, iupblg, iupdat, jacflg, jel, jparks, largst, lfond, loadup, loaduq,& + lodcor, lovl, lsub, magnet, ncycle, newtnt, newton, noshr, linear, ivscpl,& + icrpim, iradrt, ipshft, itshr, iangin, iupmdr, iconjf, jincfl, jpermg, jhour,& + isolvr, jritz, jtable, jshell, jdoubl, jform, jcentr, imini, kautth, iautof,& + ibukty, iassum, icnstd, icnstt, kmakmas, imethvp, iradrte, iradrtp, iupdate, iupdatp,& + ncycnt, marmen , idynme, ihavca, ispf, kmini, imixex, largtt, kdoela, iautofg,& + ipshftp, idntrc, ipore, jtablm, jtablc, isnecma, itrnspo, imsdif, jtrnspo, mcnear,& + imech, imecht, ielcmat, ielectt, magnett, imsdift, noplas, jtabls, jactch, jtablth,& + kgmsto , jpzo, ifricsh, iremkin, iremfor, ishearp, jspf, machining, jlshell, icompsol,& + iupblgfo, jcondir, nstcrp, nactive, ipassref, nstspnt, ibeart, icheckmpc, noline, icuring,& + ishrink, ioffsflg, isetoff, ioffsetm,iharmt, inc_incdat, iautspc, ibrake, icbush, istream_input,& + iprsinp, ivlsinp, ifirst_time,ipin_m, jgnstr_glb, imarc_return,iqvcinp, nqvceid, istpnx, imicro1,& + iaxisymm, jbreakglue,iglstif, jfastasm,iwear, iwearcf, imixmeth, ielcmadyn, idinout, igena_meth,& + magf_meth, non_assumed, iredoboudry, ioffsz0,icomplt, mesh_dual, iactrp, mgnewton, iusedens,igsigd0,& + iaem, icosim, inodels, nlharm, iampini, iphasetr, inonlcl, inonlct, iforminp,ispecerror,& + icsprg, imol, imolt, idatafit,iharmpar, inclcase, imultifreq,init_elas, ifatig, iftgmat,& + nchybrid, ibuckle, iexpande, matfor, ishormem, imsdifths +dimension :: ideva(60) +integer num_concom +parameter(num_concom=266) +common/marc_concom/& + iacous, iasmbl, iautth, ibear, icompl, iconj, icreep, ideva, idyn, idynt,& + ielas, ielcma, ielect, iform, ifour, iharm, ihcps, iheat, iheatt, ihresp,& + ijoule, ilem, ilnmom, iloren, inc, incext, incsub, ipass, iplres, ipois,& + ipoist, irpflo, ismall, ismalt, isoil, ispect, ispnow, istore, iswep, ithcrp,& + itherm, iupblg, iupdat, jacflg, jel, jparks, largst, lfond, loadup, loaduq,& + lodcor, lovl, lsub, magnet, ncycle, newtnt, newton, noshr, linear, ivscpl,& + icrpim, iradrt, ipshft, itshr, iangin, iupmdr, iconjf, jincfl, jpermg, jhour,& + isolvr, jritz, jtable, jshell, jdoubl, jform, jcentr, imini, kautth, iautof,& + ibukty, iassum, icnstd, icnstt, kmakmas, imethvp, iradrte, iradrtp, iupdate, iupdatp,& + ncycnt, marmen, idynme, ihavca, ispf, kmini, imixex, largtt, kdoela, iautofg,& + ipshftp, idntrc, ipore, jtablm, jtablc, isnecma, itrnspo, imsdif, jtrnspo, mcnear,& + imech, imecht, ielcmat, ielectt, magnett, imsdift, noplas, jtabls, jactch, jtablth,& + kgmsto , jpzo, ifricsh, iremkin, iremfor, ishearp, jspf, machining, jlshell, icompsol,& + iupblgfo, jcondir, nstcrp, nactive, ipassref, nstspnt, ibeart, icheckmpc, noline, icuring,& + ishrink, ioffsflg, isetoff, ioffsetm,iharmt, inc_incdat, iautspc, ibrake, icbush, istream_input,& + iprsinp, ivlsinp, ifirst_time,ipin_m, jgnstr_glb, imarc_return,iqvcinp, nqvceid, istpnx, imicro1,& + iaxisymm, jbreakglue,iglstif, jfastasm,iwear, iwearcf, imixmeth, ielcmadyn, idinout, igena_meth,& + magf_meth, non_assumed, iredoboudry, ioffsz0,icomplt, mesh_dual, iactrp, mgnewton, iusedens,igsigd0,& + iaem, icosim, inodels, nlharm, iampini, iphasetr, inonlcl, inonlct, iforminp,ispecerror,& + icsprg, imol, imolt, idatafit,iharmpar, inclcase, imultifreq,init_elas, ifatig, iftgmat,& + nchybrid, ibuckle, iexpande, matfor, ishormem, imsdifths +! +! comments of variables: +! +! iacous Control flag for acoustic analysis. Input data. +! iacous=1 modal acoustic analysis. +! iacous=2 harmonic acoustic-structural analysis. +! iasmbl Control flag to indicate that operator matrix should be +! recalculated. +! iautth Control flag for AUTO THERM option. +! ibear Control flag for bearing analysis. Input data. +! icompl Control variable to indicate that a complex analysis is +! being performed. Either a Harmonic analysis with damping, +! or a harmonic electro-magnetic analysis. Input data. +! iconj Flag for EBE conjugate gradient solver (=solver 1, retired) +! Also used for VKI iterative solver. +! icreep Control flag for creep analysis. Input data. +! ideva(60) - debug print out flag +! 1 print element stiffness matrices, mass matrix +! 2 output matrices used in tying +! 3 force the solution of a nonpositive definite matrix +! 4 print info of connections to each node +! 5 info of gap convergence, internal heat generated, contact +! touching and separation +! 6 nodal value array during rezoning +! 7 tying info in CONRAD GAP option, fluid element numbers in +! CHANNEL option +! 8 output incremental displacements in local coord. system +! 9 latent heat output +! 10 stress-strain in local coord. system +! 11 additional info on interlaminar stress +! 12 output right hand side and solution vector +! 13 info of CPU resources used and memory available on NT +! 14 info of mesh adaption process, 2D outline information +! info of penetration checking for remeshing +! save .fem files after afmesh3d meshing +! print local adaptivity info +! 15 surface energy balance flag +! 16 print info regarding pyrolysis +! 17 print info of "streamline topology" +! 18 print mesh data changes after remeshing +! 19 print material flow stress data read in from *.mat file +! if unit flag is on, print out flow stress after conversion +! 20 print information on table input +! 21 print out information regarding kinematic boundary conditions +! 22 print out information regarding dist loads, point loads, film +! and foundations +! 23 print out information about automatic domain decomposition +! 24 print out iteration information in SuperForm status report file +! 25 print out information for ablation +! 26 print out information for films - Table input +! 27 print out the tying forces +! 28 print out for CASI solver, convection, +! 29 DDM single file debug printout +! 30 print out cavity debug info +! 31 print out welding related info +! 32 prints categorized DDM memory usage +! 33 print out the cutting info regarding machining feature +! 34 print out the list of quantities which can be defined via a table +! and for each quantity the supported independent variables +! 35 print out detailed coupling region info +! 36 print out solver debug info level 1 (Least Detailed) +! 37 print out solver debug info level 1 (Medium Detailed) +! 38 print out solver debug info level 1 (Very Detailed) +! 39 print detailed memory allocation info +! 40 print out marc-adams debug info +! 41 output rezone mapping post file for debugging +! 42 output post file after calling oprofos() for debugging +! 43 debug printout for vcct +! 44 debug printout for progressive failure +! 45 print out automatically generated midside node coordinates (arecrd) +! 46 print out message about routine and location, where the ibort is raised (ibort_inc) +! 47 print out summary message of element variables on a +! group-basis after all the automatic changes have been +! made (em_ellibp) +! 48 Automatically generate check results based on max and min vals. +! These vals are stored in the checkr file, which is inserted +! into the *dat file by the generate_check_results script from /marc/tools +! 49 Automatically generate check results based on the real calculated values +! at the sppecified check result locations. +! These vals are stored in the checkr file, which is inserted +! into the *dat file by the update_check_results script from /marc/tools +! 50 generate a file containing the resistance or capacity matrix; +! this file can be used to compare results with a reference file +! 51 print out detailed information for segment-to-segment contact +! 52 print out detailed relative displacement information +! for uniaxial sliding contact +! 53 print out detailed sliding direction information for +! uniaxial sliding contact +! 54 print out detailed information for edges attached to a curve +! 55 print information related to viscoelasticity calculations +! 56 print out detailed information for element coloring for multithreading +! 57 print out extra overheads due to multi-threading. +! These overhead includes (i) time and (ii) memory. +! The memory report will be summed over all the children. +! 58 debug output for ELSTO usage +! 59 print out contact body forces and nodes in contact +! +! idyn Control flag for dynamics. Input data. +! 1 = eigenvalue extraction and / or modal superposition +! 2 = Newmark Beta and Single Step Houbolt (ssh with idynme=1) +! 3 = Houbolt +! 4 = Central difference +! 5 = Newer central difference +! idynt Copy of idyn at begining of increment +! ielas Control flag for ELASTIC analysis. Input data. +! Set by user or automatically turned on by Fourier option. +! Implies that each load case is treated separately. +! In Adaptive meshing analysis , forces re-analysis until +! convergence obtained. +! Also seriously misused to indicate no convergence. +! = 1 elastic option with fourier analysis +! = 2 elastic option without fourier analysis +! =-1 no convergence in recycles or max # increments reached +! Set to 1 if ELASTIC or SUBSTRUC parameter cards are used, +! or if fourier option is used. +! Then set to 2 if not fourier analysis. +! ielcma Control flag for electromagnetic analysis. Input data. +! ielcma = 1 Harmonic formulation +! ielcma = 2 Transient formulation +! ielect Control flag for electrostatic option. Input data. +! iform Control flag indicating that contact will be performed. +! ifour Control flag for Fourier analysis. +! 0 = Odd and even terms. +! 1 = symmetric (cosine) terms +! 2 = antisymmetric (sine) terms. +! iharm Control flag to indicate that a harmonic analysis will +! be performed. May change between passes. +! ihcps Control flag for coupled thermal - stress analysis. +! iheat Control flag for heat transfer analysis. Input data. +! iheatt Permanent control flag for heat transfer analysis. +! Note in coupled analysis iheatt will remain as one, +! but iheat will be zero in stress pass. +! ihresp Control flag to indicate to perform a harmonic subincrement. +! ijoule Control flag for Joule heating. +! ilem Control flag to determin which vector is to be transformed. +! Control flag to see where one is: +! ilem = 1 - elem.f +! ilem = 2 - initst.f +! ilem = 3 - pressr.f +! ilem = 3 - fstif.f +! ilem = 4 - jflux.f +! ilem = 4 - strass.f +! ilem = 5 - mass.f +! ilem = 5 - osolty.f +! ilnmom Control flag for soil - pore pressure calculation. Input data. +! ilnmom = 0 - perform only pore pressure calculation. +! = 1 - couples pore pressure - displacement analysis +! iloren Control flag for DeLorenzi J-Integral evaluation. Input data. +! inc Increment number. +! incext Control flag indicating that currently working on a +! subincrement. +! Could be due to harmonics , damping component (bearing), +! stiffness component (bearing), auto therm creep or +! old viscoplaticity +! incsub Sub-increment number. +! inonlcl control flag for nonlocal pass +! inonlct permanent control flag for nonlocal pass +! ipass Control flag for which part of coupled analysis. +! ipass = -1 - reset to base values +! ipass = 0 - do nothing +! ipass = 1 - stress part +! ipass = 2 - heat transfer part +! 3 - fluid pass +! 4 - joule heating pass +! 5 - pore pressure pass +! 6 - electrostatic pass +! 7 - magnetostatic pass +! 8 - electromagnetic pass +! 9 - diffusion pass +! ipass = 10 - nonlocal part +! iplres Flag indicating that either second matrix is stored. +! dynamic analysis - mass matrix +! heat transfer - specific heat matrix +! buckle - initial stress stiffness +! ipois Control flag indicating Poisson type analysis +! ipois = 1 for heat transfer +! = 1 for heat transfer part of coupled +! = 1 for bearing +! = 1 for electrostatic +! = 1 for magnetostatic +! = 1 for nonlocal part +! ipoist Permanent copy of ipois. In coupled analysis , ipois = 0 +! in stress portion, yet ipoist will still =1. +! irpflo global flag for rigid plastic flow analysis +! = 1 eularian formulation +! = 2 regular formulation; rigid material present in the analysis +! ismall control flag to indicate small displacement analysis. input data. +! ismall = 0 - large disp included. +! ismall = 1 - small displacement. +! the flag is changing between passes. +! ismalt permanent copy of ismall . in heat transfer portion of +! coupled analysis ismall =0 , but ismalt remains the same. +! isoil control flag indicating that soil / pore pressure +! calculation . input data. +! ispect control flag for response spectrum calculation. input data. +! ispnow control flag to indicate to perform a spectrum response +! calculation now. +! istore store stresses flag. +! istore = 0 in elem.f and if first pass of creep +! convergence checking in ogetst.f +! or harmonic analysis or thruc.f if not +! converged. +! iswep control flag for eigenvalue analysis. +! iswep=1 - go do extraction process +! ithcrp control flag for auto therm creep option. input data. +! itherm control flag for either temperature dependent material +! properties and/or thermal loads. +! iupblg control flag for follower force option. input data. +! iupdat control flag for update lagrange option for current element. +! jacflg control flag for lanczos iteration method. input data. +! jel control flag indicating that total load applied in +! increment, ignore previous solution. +! jel = 1 in increment 0 +! = 1 if elastic or fourier +! = 1 in subincrements with elastic and adaptive +! jparks control flag for j integral by parks method. input data. +! largst control flag for finite strain plasticity. input data. +! lfond control variable that indicates if doing elastic +! foundation or film calculation. influences whether +! this is volumetric or surface integration. +! loadup control flag that indicates that nonlinearity occurred +! during previous increment. +! loaduq control flag that indicates that nonlinearity occurred. +! lodcor control flag for switching on the residual load correction. +! notice in input stage lodcor=0 means no loadcor, +! after omarc lodcor=1 means no loadcor +! lovl control flag for determining which "overlay" is to +! be called from ellib. +! lovl = 1 omarc +! = 2 oaread +! = 3 opress +! = 4 oasemb +! = 5 osolty +! = 6 ogetst +! = 7 oscinc +! = 8 odynam +! = 9 opmesh +! = 10 omesh2 +! = 11 osetz +! = 12 oass +! = 13 oincdt +! = 14 oasmas +! = 15 ofluas +! = 16 ofluso +! = 17 oshtra +! = 18 ocass +! = 19 osoltc +! = 20 orezon +! = 21 otest +! = 22 oeigen +! lsub control variable to determine which part of element +! assembly function is being done. +! lsub = 1 - no longer used +! = 2 - beta* +! = 3 - cons* +! = 4 - ldef* +! = 5 - posw* +! = 6 - theta* +! = 7 - tmarx* +! = 8 - geom* +! magnet control flag for magnetostatic analysis. input data. +! ncycle cycle number. accumulated in osolty.f +! note first time through oasemb.f , ncycle = 0. +! newtnt control flag for permanent copy of newton. +! newton iteration type. input data. +! newton : = 1 full newton raphson +! 2 modified newton raphson +! 3 newton raphson with strain correct. +! 4 direct substitution +! 5 direct substitution followed by n.r. +! 6 direct substitution with line search +! 7 full newton raphson with secant initial stress +! 8 secant method +! 9 full newton raphson with line search +! noshr control flag for calculation interlaminar shears for +! elements 22,45, and 75. input data. +!ees +! +! jactch = 1 or 2 if elements are activated or deactivated +! = 3 if elements are adaptively remeshed or rezoned +! = 0 normally / reset to 0 when assembly is done +! ifricsh = 0 call to fricsh in otest not needed +! = 1 call to fricsh (nodal friction) in otest needed +! iremkin = 0 remove deactivated kinematic boundary conditions +! immediately - only in new input format (this is default) +! = 1 remove deactivated kinematic boundary conditions +! gradually - only in new input format +! iremfor = 0 remove force boundary conditions immediately - +! only in new input format (this is default) +! = 1 remove force boundary conditions gradually - +! only in new input format (this is default) +! ishearp set to 1 if shear panel elements are present in the model +! +! jspf = 0 not in spf loadcase +! > 0 in spf loadcase (jspf=1 during first increment) +! machining = 1 if the metal cutting feature is used, for memory allocation purpose +! = 0 (default) if no metal cutting feature required +! +! jlshell = 1 if there is a shell element in the mesh +! icompsol = 1 if there is a composite solid element in the mesh +! iupblgfo = 1 if follower force for point loads +! jcondir = 1 if contact priority option is used +! nstcrp = 0 (default) steady state creep flag (undocumented feature. +! if not 0, turns off special ncycle = 0 code in radial.f) +! nactive = number of active passes, if =1 then it's not a coupled analysis +! ipassref = reference ipass, if not in a multiphysics pass ipass=ipassref +! icheckmpc = value of mpc-check parameter option +! noline = set to 1 in osolty if no line seacrh should be done in ogetst +! icuring = set to 1 if the curing is included for the heat transfer analysis. +! ishrink = set to 1 if shrinkage strain is included for mechancial analysis. +! ioffsflg = 1 for small displacement beam/shell offsets +! = 2 for large displacement beam/shell offsets +! isetoff = 0 - do not apply beam/shell offsets +! = 1 - apply beam/shell offsets +! ioffsetm = min. value of offset flag +! iharmt = 1 global flag if a coupled analysis contains an harmonic pass +! inc_incdat = flag to record increment number of a new loadcase in incdat.f +! iautspc = flag for AutoSPC option +! ibrake = brake squeal in this increment +! icbush = set to 1 if cbush elements present in model +! istream_input = set to 1 for streaming input calling Marc as library +! iprsinp = set to 1 if pressure input, introduced so other variables +! such as h could be a function of pressure +! ivlsinp = set to 1 if velocity input, introduced so other variables +! such as h could be a function of velocity +! ipin_m = # of beam element with PIN flag +! jgnstr_glb = global control over pre or fast integrated composite shells +! imarc_return = Marc return flag for streaming input control +! iqvcimp = if non-zero, then the number of QVECT boundary conditions +! nqvceid = number of QVECT boundary conditions, where emisivity/absorbtion id entered +! istpnx = 1 if to stop at end of increment +! imicro1 = 1 if micro1 interface is used +! iaxisymm = set to 1 if axisymmetric analysis +! jbreakglue = set to 1 if breaking glued option is used +! iglstif = 1 if ddm and global stiffness matrix formed (sgi solver 6 or solver9) +! jfastasm = 1 do fast assembly using SuperForm code +! iwear = set to 1 if wear model, set to 2 if wear model and coordinates updated +! iwearcf = set to 1 to store nodal coefficient of friction for wear calculation +! imixmeth = set=1 then use nonlinear mixture material - allocate memory +! ielcmadyn = flag for magnetodynamics +! 0 - electromagnetics using newmark beta +! 1 - transient magnetics using backward euler +! idinout = flag to control if inside out elements should be deactivated +! igena_meth = 0 - generalized alpha parameters depend on whether or not contact +! is flagged (dynamic,7) +! 10 - generalized alpha parameters are optimized for a contact +! analysis (dynamic,8) +! 11 - generalized alpha parameters are optimized for an analysis +! without contact (dynamic,8) +! magf_meth = - Method to compute force in magnetostatic - structural +! = 1 - Virtual work method based on finite difference for the force computation +! = 2 - Maxwell stress tensor +! = 3 - Virtual work method based on local derivative for the force computation +! non_assumed = 1 no assumed strain formulation (forced) +! iredoboudry set to 1 if contact boundary needs to be recalculated +! ioffsz0 = 1 if composite are used with reference position.ne.0 +! icomplt = 1 global flag if a coupled analysis contains an complex pass +! mesh_dual = 1 two independent meshes are used in magnetodynamic/thermal/structural +! one for magnetodynamic and the other for the remaining passes +! iactrp = 1 in an analysis with global remeshing, include inactive +! rigid bodies on post file +! mgnewton = 1 Use full Newton Raphson iteration for magnetostatic pass +! +! iusedens > 0 if mass density is used in the analysis (dynamics, mass dependent loading) +! igsigd0 = 1 set varselem(igsigd) to zero in next oasemb +! iaem = 1 if marc is called from aem (0 - off - default) +! icosim = 1 if marc is used in co-simulation analysis with ADAMS using the CosimEngine +! = 2 if marc is used in co-simulation analysis with ADAMS using the ACSI interface +! = 3 if marc is used in co-simulation analysis with scFLOW using the CosimEngine +! = 4 if marc is used in co-simulation analysis with scFLOW and ADAMS using the CosimEngine +! inodels = 1 nodal integration elements 239/240/241 present +! nlharm = 0 harmonic subincrements are linear +! = 1 harmonic subincrements are nonlinear +! iampini = 0 amplitude of previous harmonic subinc is initial estimate (default) +! = 1 zero amplitude is initial estimate +! iphasetr = 1 phase transformation material model is used +! iforminp flag indicating that contact is switched on via the CONTACT +! option in the input file (as opposed to the case that contact +! is switched on internally due to cyclic symmetry or model +! section creation) +! ispecerror = a+10*b (only for spectrum response analysis with missing mass option) +! a=0 or a=1 (modal shape with non-zero shift) +! b=0 or b=1 (recover with new assembly of stiffness matrix) +! icsprg = set to 1 if spring elements present in model +! imol Control flag for molecualr diffusion pass +! imolt Permanent control flag for molecualr diffusion pass +! Note in coupled analysis imolt will remain as one, +! but imol will be zero in stress pass or thermal pass. +! idatafit = run Marc to fit parameters +! iharmpar = 1 if harmonic parameter option is used +! inclcase load case increment use for cyclic plasticity data fitting +! imultifreq flag to indicate how many harmonic magnetodynamic passes are computed in coupled +! magnetodynamic/thermal(/structural) analyses. +! 0 or 1 one pass 2 two passes 3 or more is not supported +! init_elas use elastic stress-strain law as the material tangent for +! the first cycle of an increment +! ifatig packed integer telling which fatigue mode is active +! 1 = elastomer +! 10 = stress-life +! 100 = strain-life +! = 2 strain-life fatigue +! iftgmat = 0 no fatigue material properties in the dat file +! = 1 fatigue material properties in the dat file +! nchybrid cycle count used for hybrid contact; meant to force an extra iteration +! if the overlap for a node in hybrid contact is too large +! ibuckle buckle parameter option is active +! iexpande set to 1 if expanded elements (248, 249, 250 or 251) are +! present, 0 otherwise +! matfor flag for material forces computation +! 0: Eshleby stress and material force vector not requested +! 1: output Eshelby stress tensor, but no material force vector +! 2: output material force vector, but no Eshelby stress tensor +! 3: output Eshelby stress tensor and material force vector +! ishormem set to 1 if shell or membrane elements are present, 0 otherwise +! imsdifths flag for diffusion(pressure)/thermal/structural analysis +! +!*********************************************************************** +!$omp threadprivate(/marc_concom/) +!! diff --git a/src/Marc/include/creeps2023.3 b/src/Marc/include/creeps2023.3 new file mode 100644 index 000000000..b35d2b6bf --- /dev/null +++ b/src/Marc/include/creeps2023.3 @@ -0,0 +1,73 @@ +! common block definition file taken from respective MSC.Marc release and reformated to free format +!*********************************************************************** +! +! File: creeps.cmn +! +! MSC.Marc include file +! +real(pReal) cptim,timinc,timinc_p,timinc_s,timincm,timinc_a,timinc_b +integer icfte,icfst,icfeq,icftm,icetem,mcreep,jcreep,icpa,icftmp,icfstr,& + icfqcp,icfcpm,icrppr,icrcha,icpb,iicpmt,iicpa +real(pReal) time_beg_lcase,time_beg_inc,fractol,time_beg_pst +real(pReal) fraction_donn,timinc_ol2 +! +integer num_creepsr,num_creepsi,num_creeps2r,ncrp_arry +parameter(num_creepsr=7) +parameter(num_creepsi=17) +parameter(num_creeps2r=6) +parameter(ncrp_arry=7) +common/marc_creeps/cptim,timinc,timinc_p,timinc_s,timincm,timinc_a,timinc_b,icfte,icfst,& + icfeq,icftm,icetem,mcreep,jcreep,icpa,icftmp,icfstr,icfqcp,icfcpm,icrppr,icrcha,icpb,iicpmt,iicpa +common/marc_creeps2/time_beg_lcase,time_beg_inc,fractol,time_beg_pst,fraction_donn,timinc_ol2 +! +! cptim Total time at begining of increment. +! timinc Incremental time for this step. +! icfte Local copy number of slopes of creep strain rate function +! versus temperature. Is -1 if exponent law used. +! icfst Local copy number of slopes of creep strain rate function +! versus equivalent stress. Is -1 if exponent law used. +! icfeq Local copy number of slopes of creep strain rate function +! versus equivalent strain. Is -1 if exponent law used. +! icftm Local copy number of slopes of creep strain rate function +! versus time. Is -1 if exponent law used. +! icetem Element number that needs to be checked for creep convergence +! or, if negative, the number of elements that need to +! be checked. In the latter case the elements to check +! are stored in ielcp. +! mcreep Maximum nuber of iterations for explicit creep. +! jcreep Counter of number of iterations for explicit creep +! procedure. jcreep must be .le. mcreep +! icpa(1-6) Pointer to constants in creep strain rate expression. +! icftmp Pointer to temperature dependent creep strain rate data. +! icfstr Pointer to equivalent stress dependent creep strain rate data. +! icfqcp Pointer to equivalent creep strain dependent creep strain +! rate data. +! icfcpm Pointer to equivalent creep strain rate dependent +! creep strain rate data. +! icrppr Permanent copy of icreep +! icrcha Control flag for creep convergence checking , if set to +! 1 then testing on absolute change in stress and creep +! strain, not relative testing. Input data. +! icpb(1-4) Pointer to storage of material id cross reference numbers. +! iicpmt creep law type ID +! =1 - power law +! =2 - solder +! =3 - steady-creep +! =4 - hyperbolic steady-creep +! iicpa Pointer to table IDs for constants in creep strain rate +! expression +! +! +! time_beg_lcase time at the beginning of the current load case +! time_beg_inc time at the beginning of the current increment +! fractol fraction of loadcase or increment time when we +! consider it to be finished +! time_beg_pst time corresponding to first increment to be +! read in from thermal post file for auto step +! +! timinc_old Time step of the previous increment +! +!*********************************************************************** +!!$omp threadprivate(/marc_creeps/) +!!$omp threadprivate(/marc_creeps2/) +!! From ab41273df7f480dbd386598d5ccaed18e1fe433a Mon Sep 17 00:00:00 2001 From: Franz Roters Date: Wed, 18 Oct 2023 13:56:27 +0200 Subject: [PATCH 08/84] make Marc 2023.2 and use it for testing with new reference results --- .gitlab-ci.yml | 2 +- PRIVATE | 2 +- python/damask/solver/_marc.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml index 5ddfcc27e..6d1213b7d 100644 --- a/.gitlab-ci.yml +++ b/.gitlab-ci.yml @@ -47,7 +47,7 @@ variables: PETSC_INTELLLVM: "Libraries/PETSc/3.16.3/oneAPI-2022.0.1-IntelMPI-2021.5.0" PETSC_INTEL: "Libraries/PETSc/3.16.5/Intel-2022.0.1-IntelMPI-2021.5.0" # ++++++++++++ MSC Marc +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ - MSC: "FEM/MSC/2023.1" + MSC: "FEM/MSC/2023.2" IntelMarc: "Compiler/Intel/19.1.2 Libraries/IMKL/2020" HDF5Marc: "HDF5/1.12.2/Intel-19.1.2" diff --git a/PRIVATE b/PRIVATE index 9d2a98d72..42b20feeb 160000 --- a/PRIVATE +++ b/PRIVATE @@ -1 +1 @@ -Subproject commit 9d2a98d72d9bf547dd697124cb795cf6a9668d30 +Subproject commit 42b20feeb44e71504f3ec757ced18c04e55804fe diff --git a/python/damask/solver/_marc.py b/python/damask/solver/_marc.py index c07876c93..37131b131 100644 --- a/python/damask/solver/_marc.py +++ b/python/damask/solver/_marc.py @@ -4,7 +4,7 @@ import re from pathlib import Path from typing import Literal -_marc_version = '2023.1' +_marc_version = '2023.2' _marc_root = '/opt/msc' _damask_root = str(Path(__file__).parents[3]) From 755fa7e5d8bec3f97c887f67c7d151441a96253c Mon Sep 17 00:00:00 2001 From: Franz Roters Date: Wed, 18 Oct 2023 16:21:29 +0200 Subject: [PATCH 09/84] make Marc 2023.3 the default version and use it for testing --- .gitlab-ci.yml | 2 +- python/damask/solver/_marc.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml index 6d1213b7d..6c4681e33 100644 --- a/.gitlab-ci.yml +++ b/.gitlab-ci.yml @@ -47,7 +47,7 @@ variables: PETSC_INTELLLVM: "Libraries/PETSc/3.16.3/oneAPI-2022.0.1-IntelMPI-2021.5.0" PETSC_INTEL: "Libraries/PETSc/3.16.5/Intel-2022.0.1-IntelMPI-2021.5.0" # ++++++++++++ MSC Marc +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ - MSC: "FEM/MSC/2023.2" + MSC: "FEM/MSC/2023.3" IntelMarc: "Compiler/Intel/19.1.2 Libraries/IMKL/2020" HDF5Marc: "HDF5/1.12.2/Intel-19.1.2" diff --git a/python/damask/solver/_marc.py b/python/damask/solver/_marc.py index 37131b131..c3da5a281 100644 --- a/python/damask/solver/_marc.py +++ b/python/damask/solver/_marc.py @@ -4,7 +4,7 @@ import re from pathlib import Path from typing import Literal -_marc_version = '2023.2' +_marc_version = '2023.3' _marc_root = '/opt/msc' _damask_root = str(Path(__file__).parents[3]) From d1807b1860a43b08e949e0a4d74ca729ec51890d Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Mon, 16 Oct 2023 14:54:52 +0200 Subject: [PATCH 10/84] libfyaml 0.9 has a list of libraries --- CMakeLists.txt | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 60a05cfc7..a501717ed 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -130,7 +130,10 @@ set(CMAKE_Fortran_FLAGS_${CMAKE_BUILD_TYPE} "${CMAKE_Fortran_FLAGS_${CMAKE_BUILD set(CMAKE_Fortran_LINK_EXECUTABLE "${CMAKE_Fortran_LINK_EXECUTABLE} -o -L${PETSC_LIBRARY_DIRS} -lpetsc ${PETSC_EXTERNAL_LIB} -lz") if(fYAML_FOUND STREQUAL "1") - set(CMAKE_Fortran_LINK_EXECUTABLE "${CMAKE_Fortran_LINK_EXECUTABLE} -L${fYAML_LIBRARY_DIRS} -l${fYAML_LIBRARIES}") + set(CMAKE_Fortran_LINK_EXECUTABLE "${CMAKE_Fortran_LINK_EXECUTABLE} -L${fYAML_LIBRARY_DIRS}") + foreach(fYAML_LIBRARY ${fYAML_LIBRARIES}) + set(CMAKE_Fortran_LINK_EXECUTABLE "${CMAKE_Fortran_LINK_EXECUTABLE} -L${fYAML_LIBRARY_DIRS} -l${fYAML_LIBRARY}") + endforeach() add_definitions(-DFYAML) pkg_get_variable(fYAML_INCLUDE_DIR libfyaml includedir) # fYAML_INCLUDE_DIRS and fYAML_CFLAGS are not working set(CMAKE_C_FLAGS_${CMAKE_BUILD_TYPE} "${CMAKE_C_FLAGS_${CMAKE_BUILD_TYPE}} -I${fYAML_INCLUDE_DIR}") From 15cfeff8981b31bc6938803170949702f7bf7693 Mon Sep 17 00:00:00 2001 From: Test User Date: Fri, 20 Oct 2023 20:04:54 +0200 Subject: [PATCH 11/84] [skip ci] updated version information after successful test of v3.0.0-alpha7-891-g72fa02f58 --- VERSION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/VERSION b/VERSION index c62aab3b8..2049d308f 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -3.0.0-alpha7-888-g5bb7e54df +3.0.0-alpha7-891-g72fa02f58 From 2cfcac67ce3a47873b56209b4e7d84ce9fca9eaf Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Fri, 20 Oct 2023 23:29:09 +0200 Subject: [PATCH 12/84] updated to 2023.3 --- install/MarcMentat/2023.3/Marc_tools/include_linux64.patch | 6 +++--- install/MarcMentat/2023.3/Marc_tools/run_damask_hmp.patch | 2 +- install/MarcMentat/2023.3/Marc_tools/run_damask_lmp.patch | 2 +- install/MarcMentat/2023.3/Marc_tools/run_damask_mp.patch | 2 +- 4 files changed, 6 insertions(+), 6 deletions(-) diff --git a/install/MarcMentat/2023.3/Marc_tools/include_linux64.patch b/install/MarcMentat/2023.3/Marc_tools/include_linux64.patch index f9646b240..de6d9b609 100644 --- a/install/MarcMentat/2023.3/Marc_tools/include_linux64.patch +++ b/install/MarcMentat/2023.3/Marc_tools/include_linux64.patch @@ -49,15 +49,15 @@ + +# DAMASK compiler calls +DFORTLOWMP="$FCOMP -c -O0 -qno-offload -implicitnone -stand f18 -standard-semantics -assume nostd_mod_proc_name -safe-cray-ptr $PROFILE -zero -mp1 -WB $I8FFLAGS -I$MARC_SOURCE/common \ -+ -fpp -ftz -diag-disable 5268 -warn declarations -warn general -warn usage -warn interfaces -warn ignore_loc -warn alignments -DMARC4DAMASK=2023.2 -DDAMASKVERSION=$DAMASKVERSION \ ++ -fpp -ftz -diag-disable 5268 -warn declarations -warn general -warn usage -warn interfaces -warn ignore_loc -warn alignments -DMARC4DAMASK=2023.3 -DDAMASKVERSION=$DAMASKVERSION \ + -qopenmp -qopenmp-threadprivate=compat\ + $MUMPS_INCLUDE $I8DEFINES -DLinux -DLINUX -DLinux_intel $FDEFINES $DDM $SOLVERFLAGS -I$KDTREE2_MOD -I$MARC_MOD" +DFORTRANMP="$FCOMP -c -O1 -qno-offload -implicitnone -stand f18 -standard-semantics -assume nostd_mod_proc_name -safe-cray-ptr $PROFILE -zero -mp1 -WB $I8FFLAGS -I$MARC_SOURCE/common \ -+ -fpp -ftz -diag-disable 5268 -warn declarations -warn general -warn usage -warn interfaces -warn ignore_loc -warn alignments -DMARC4DAMASK=2023.2 -DDAMASKVERSION=$DAMASKVERSION \ ++ -fpp -ftz -diag-disable 5268 -warn declarations -warn general -warn usage -warn interfaces -warn ignore_loc -warn alignments -DMARC4DAMASK=2023.3 -DDAMASKVERSION=$DAMASKVERSION \ + -qopenmp -qopenmp-threadprivate=compat\ + $MUMPS_INCLUDE $I8DEFINES -DLinux -DLINUX -DLinux_intel $FDEFINES $DDM $SOLVERFLAGS -I$KDTREE2_MOD -I$MARC_MOD" +DFORTHIGHMP="$FCOMP -c -O3 -qno-offload -implicitnone -stand f18 -standard-semantics -assume nostd_mod_proc_name -safe-cray-ptr $PROFILE -zero -mp1 -WB $I8FFLAGS -I$MARC_SOURCE/common \ -+ -fpp -ftz -diag-disable 5268 -warn declarations -warn general -warn usage -warn interfaces -warn ignore_loc -warn alignments -DMARC4DAMASK=2023.2 -DDAMASKVERSION=$DAMASKVERSION \ ++ -fpp -ftz -diag-disable 5268 -warn declarations -warn general -warn usage -warn interfaces -warn ignore_loc -warn alignments -DMARC4DAMASK=2023.3 -DDAMASKVERSION=$DAMASKVERSION \ + -qopenmp -qopenmp-threadprivate=compat\ + $MUMPS_INCLUDE $I8DEFINES -DLinux -DLINUX -DLinux_intel $FDEFINES $DDM $SOLVERFLAGS -I$KDTREE2_MOD -I$MARC_MOD" + diff --git a/install/MarcMentat/2023.3/Marc_tools/run_damask_hmp.patch b/install/MarcMentat/2023.3/Marc_tools/run_damask_hmp.patch index f05c41969..9e6e17d6d 100644 --- a/install/MarcMentat/2023.3/Marc_tools/run_damask_hmp.patch +++ b/install/MarcMentat/2023.3/Marc_tools/run_damask_hmp.patch @@ -6,7 +6,7 @@ ############################################################################## +# remove all Mentat paths from LD_LIBRARY_PATH +LD_LIBRARY_PATH=:$LD_LIBRARY_PATH: -+LD_LIBRARY_PATH=${LD_LIBRARY_PATH//+([!(:)])mentat2023.2+([!(:)])/:} ++LD_LIBRARY_PATH=${LD_LIBRARY_PATH//+([!(:)])mentat2023.3+([!(:)])/:} +LD_LIBRARY_PATH=${LD_LIBRARY_PATH//+([(:)])/:} +LD_LIBRARY_PATH=${LD_LIBRARY_PATH#:}; LD_LIBRARY_PATH=${LD_LIBRARY_PATH%:} # set DIR to the directory in which this script is diff --git a/install/MarcMentat/2023.3/Marc_tools/run_damask_lmp.patch b/install/MarcMentat/2023.3/Marc_tools/run_damask_lmp.patch index 5ff268524..f2c03a921 100644 --- a/install/MarcMentat/2023.3/Marc_tools/run_damask_lmp.patch +++ b/install/MarcMentat/2023.3/Marc_tools/run_damask_lmp.patch @@ -6,7 +6,7 @@ ############################################################################## +# remove all Mentat paths from LD_LIBRARY_PATH +LD_LIBRARY_PATH=:$LD_LIBRARY_PATH: -+LD_LIBRARY_PATH=${LD_LIBRARY_PATH//+([!(:)])mentat2023.2+([!(:)])/:} ++LD_LIBRARY_PATH=${LD_LIBRARY_PATH//+([!(:)])mentat2023.3+([!(:)])/:} +LD_LIBRARY_PATH=${LD_LIBRARY_PATH//+([(:)])/:} +LD_LIBRARY_PATH=${LD_LIBRARY_PATH#:}; LD_LIBRARY_PATH=${LD_LIBRARY_PATH%:} # set DIR to the directory in which this script is diff --git a/install/MarcMentat/2023.3/Marc_tools/run_damask_mp.patch b/install/MarcMentat/2023.3/Marc_tools/run_damask_mp.patch index 579952b1f..6cf3c9ac8 100644 --- a/install/MarcMentat/2023.3/Marc_tools/run_damask_mp.patch +++ b/install/MarcMentat/2023.3/Marc_tools/run_damask_mp.patch @@ -6,7 +6,7 @@ ############################################################################## +# remove all Mentat paths from LD_LIBRARY_PATH +LD_LIBRARY_PATH=:$LD_LIBRARY_PATH: -+LD_LIBRARY_PATH=${LD_LIBRARY_PATH//+([!(:)])mentat2023.2+([!(:)])/:} ++LD_LIBRARY_PATH=${LD_LIBRARY_PATH//+([!(:)])mentat2023.3+([!(:)])/:} +LD_LIBRARY_PATH=${LD_LIBRARY_PATH//+([(:)])/:} +LD_LIBRARY_PATH=${LD_LIBRARY_PATH#:}; LD_LIBRARY_PATH=${LD_LIBRARY_PATH%:} # set DIR to the directory in which this script is From 3268ed2eb078948648e8a5c2e79c2bbae3f849ca Mon Sep 17 00:00:00 2001 From: Test User Date: Sat, 21 Oct 2023 03:24:36 +0200 Subject: [PATCH 13/84] [skip ci] updated version information after successful test of v3.0.0-alpha7-898-g7d3692317 --- VERSION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/VERSION b/VERSION index 2049d308f..0b5f9047a 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -3.0.0-alpha7-891-g72fa02f58 +3.0.0-alpha7-898-g7d3692317 From ff2c66f8f006d82490a34c2cac067cd6d5d818ab Mon Sep 17 00:00:00 2001 From: Test User Date: Sat, 21 Oct 2023 08:43:41 +0200 Subject: [PATCH 14/84] [skip ci] updated version information after successful test of v3.0.0-alpha7-901-ga25d1b1b9 --- VERSION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/VERSION b/VERSION index 0b5f9047a..133c77787 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -3.0.0-alpha7-898-g7d3692317 +3.0.0-alpha7-901-ga25d1b1b9 From 199b84d096e9f846bf062ef21b0a57851f6ec9d7 Mon Sep 17 00:00:00 2001 From: Philip Eisenlohr Date: Mon, 23 Oct 2023 22:53:27 -0400 Subject: [PATCH 15/84] proper bi-directional orientation relationships --- python/damask/_crystal.py | 698 +++++++++++------- python/damask/_orientation.py | 42 +- .../tests/resources/Orientation/cF_Bain.txt | 4 +- python/tests/resources/Orientation/cF_GT.txt | 46 +- .../resources/Orientation/cF_GT_prime.txt | 44 +- python/tests/resources/Orientation/cF_KS.txt | 46 +- python/tests/resources/Orientation/cF_NW.txt | 24 +- .../tests/resources/Orientation/cF_Pitsch.txt | 24 +- .../tests/resources/Orientation/cI_Bain.txt | 6 +- python/tests/resources/Orientation/cI_GT.txt | 44 +- .../resources/Orientation/cI_GT_prime.txt | 40 +- python/tests/resources/Orientation/cI_KS.txt | 48 +- python/tests/resources/Orientation/cI_NW.txt | 24 +- .../tests/resources/Orientation/cI_Pitsch.txt | 24 +- python/tests/test_Orientation.py | 7 - 15 files changed, 633 insertions(+), 488 deletions(-) diff --git a/python/damask/_crystal.py b/python/damask/_crystal.py index b2fb4edd7..12def3bda 100644 --- a/python/damask/_crystal.py +++ b/python/damask/_crystal.py @@ -28,275 +28,405 @@ lattice_symmetries: Dict[BravaisLattice, CrystalFamily] = { } orientation_relationships: Dict[str, Dict[BravaisLattice,np.ndarray]] = { - 'KS': { - 'cF': np.array([ - [[-1, 0, 1],[ 1, 1, 1]], - [[-1, 0, 1],[ 1, 1, 1]], - [[ 0, 1,-1],[ 1, 1, 1]], - [[ 0, 1,-1],[ 1, 1, 1]], - [[ 1,-1, 0],[ 1, 1, 1]], - [[ 1,-1, 0],[ 1, 1, 1]], - [[ 1, 0,-1],[ 1,-1, 1]], - [[ 1, 0,-1],[ 1,-1, 1]], - [[-1,-1, 0],[ 1,-1, 1]], - [[-1,-1, 0],[ 1,-1, 1]], - [[ 0, 1, 1],[ 1,-1, 1]], - [[ 0, 1, 1],[ 1,-1, 1]], - [[ 0,-1, 1],[-1, 1, 1]], - [[ 0,-1, 1],[-1, 1, 1]], - [[-1, 0,-1],[-1, 1, 1]], - [[-1, 0,-1],[-1, 1, 1]], - [[ 1, 1, 0],[-1, 1, 1]], - [[ 1, 1, 0],[-1, 1, 1]], - [[-1, 1, 0],[ 1, 1,-1]], - [[-1, 1, 0],[ 1, 1,-1]], - [[ 0,-1,-1],[ 1, 1,-1]], - [[ 0,-1,-1],[ 1, 1,-1]], - [[ 1, 0, 1],[ 1, 1,-1]], - [[ 1, 0, 1],[ 1, 1,-1]], - ],dtype=float), - 'cI': np.array([ - [[-1,-1, 1],[ 0, 1, 1]], - [[-1, 1,-1],[ 0, 1, 1]], - [[-1,-1, 1],[ 0, 1, 1]], - [[-1, 1,-1],[ 0, 1, 1]], - [[-1,-1, 1],[ 0, 1, 1]], - [[-1, 1,-1],[ 0, 1, 1]], - [[-1,-1, 1],[ 0, 1, 1]], - [[-1, 1,-1],[ 0, 1, 1]], - [[-1,-1, 1],[ 0, 1, 1]], - [[-1, 1,-1],[ 0, 1, 1]], - [[-1,-1, 1],[ 0, 1, 1]], - [[-1, 1,-1],[ 0, 1, 1]], - [[-1,-1, 1],[ 0, 1, 1]], - [[-1, 1,-1],[ 0, 1, 1]], - [[-1,-1, 1],[ 0, 1, 1]], - [[-1, 1,-1],[ 0, 1, 1]], - [[-1,-1, 1],[ 0, 1, 1]], - [[-1, 1,-1],[ 0, 1, 1]], - [[-1,-1, 1],[ 0, 1, 1]], - [[-1, 1,-1],[ 0, 1, 1]], - [[-1,-1, 1],[ 0, 1, 1]], - [[-1, 1,-1],[ 0, 1, 1]], - [[-1,-1, 1],[ 0, 1, 1]], - [[-1, 1,-1],[ 0, 1, 1]], - ],dtype=float), - }, - 'GT': { - 'cF': np.array([ - [[ -5,-12, 17],[ 1, 1, 1]], - [[ 17, -5,-12],[ 1, 1, 1]], - [[-12, 17, -5],[ 1, 1, 1]], - [[ 5, 12, 17],[ -1, -1, 1]], - [[-17, 5,-12],[ -1, -1, 1]], - [[ 12,-17, -5],[ -1, -1, 1]], - [[ -5, 12,-17],[ -1, 1, 1]], - [[ 17, 5, 12],[ -1, 1, 1]], - [[-12,-17, 5],[ -1, 1, 1]], - [[ 5,-12,-17],[ 1, -1, 1]], - [[-17, -5, 12],[ 1, -1, 1]], - [[ 12, 17, 5],[ 1, -1, 1]], - [[ -5, 17,-12],[ 1, 1, 1]], - [[-12, -5, 17],[ 1, 1, 1]], - [[ 17,-12, -5],[ 1, 1, 1]], - [[ 5,-17,-12],[ -1, -1, 1]], - [[ 12, 5, 17],[ -1, -1, 1]], - [[-17, 12, -5],[ -1, -1, 1]], - [[ -5,-17, 12],[ -1, 1, 1]], - [[-12, 5,-17],[ -1, 1, 1]], - [[ 17, 12, 5],[ -1, 1, 1]], - [[ 5, 17, 12],[ 1, -1, 1]], - [[ 12, -5,-17],[ 1, -1, 1]], - [[-17,-12, 5],[ 1, -1, 1]], - ],dtype=float), - 'cI': np.array([ - [[-17, -7, 17],[ 1, 0, 1]], - [[ 17,-17, -7],[ 1, 1, 0]], - [[ -7, 17,-17],[ 0, 1, 1]], - [[ 17, 7, 17],[ -1, 0, 1]], - [[-17, 17, -7],[ -1, -1, 0]], - [[ 7,-17,-17],[ 0, -1, 1]], - [[-17, 7,-17],[ -1, 0, 1]], - [[ 17, 17, 7],[ -1, 1, 0]], - [[ -7,-17, 17],[ 0, 1, 1]], - [[ 17, -7,-17],[ 1, 0, 1]], - [[-17,-17, 7],[ 1, -1, 0]], - [[ 7, 17, 17],[ 0, -1, 1]], - [[-17, 17, -7],[ 1, 1, 0]], - [[ -7,-17, 17],[ 0, 1, 1]], - [[ 17, -7,-17],[ 1, 0, 1]], - [[ 17,-17, -7],[ -1, -1, 0]], - [[ 7, 17, 17],[ 0, -1, 1]], - [[-17, 7,-17],[ -1, 0, 1]], - [[-17,-17, 7],[ -1, 1, 0]], - [[ -7, 17,-17],[ 0, 1, 1]], - [[ 17, 7, 17],[ -1, 0, 1]], - [[ 17, 17, 7],[ 1, -1, 0]], - [[ 7,-17,-17],[ 0, -1, 1]], - [[-17, -7, 17],[ 1, 0, 1]], - ],dtype=float), - }, - 'GT_prime': { - 'cF' : np.array([ - [[ 0, 1, -1],[ 7, 17, 17]], - [[ -1, 0, 1],[ 17, 7, 17]], - [[ 1, -1, 0],[ 17, 17, 7]], - [[ 0, -1, -1],[ -7,-17, 17]], - [[ 1, 0, 1],[-17, -7, 17]], - [[ 1, -1, 0],[-17,-17, 7]], - [[ 0, 1, -1],[ 7,-17,-17]], - [[ 1, 0, 1],[ 17, -7,-17]], - [[ -1, -1, 0],[ 17,-17, -7]], - [[ 0, -1, -1],[ -7, 17,-17]], - [[ -1, 0, 1],[-17, 7,-17]], - [[ -1, -1, 0],[-17, 17, -7]], - [[ 0, -1, 1],[ 7, 17, 17]], - [[ 1, 0, -1],[ 17, 7, 17]], - [[ -1, 1, 0],[ 17, 17, 7]], - [[ 0, 1, 1],[ -7,-17, 17]], - [[ -1, 0, -1],[-17, -7, 17]], - [[ -1, 1, 0],[-17,-17, 7]], - [[ 0, -1, 1],[ 7,-17,-17]], - [[ -1, 0, -1],[ 17, -7,-17]], - [[ 1, 1, 0],[ 17,-17, -7]], - [[ 0, 1, 1],[ -7, 17,-17]], - [[ 1, 0, -1],[-17, 7,-17]], - [[ 1, 1, 0],[-17, 17, -7]], - ],dtype=float), - 'cI' : np.array([ - [[ 1, 1, -1],[ 12, 5, 17]], - [[ -1, 1, 1],[ 17, 12, 5]], - [[ 1, -1, 1],[ 5, 17, 12]], - [[ -1, -1, -1],[-12, -5, 17]], - [[ 1, -1, 1],[-17,-12, 5]], - [[ 1, -1, -1],[ -5,-17, 12]], - [[ -1, 1, -1],[ 12, -5,-17]], - [[ 1, 1, 1],[ 17,-12, -5]], - [[ -1, -1, 1],[ 5,-17,-12]], - [[ 1, -1, -1],[-12, 5,-17]], - [[ -1, -1, 1],[-17, 12, -5]], - [[ -1, -1, -1],[ -5, 17,-12]], - [[ 1, -1, 1],[ 12, 17, 5]], - [[ 1, 1, -1],[ 5, 12, 17]], - [[ -1, 1, 1],[ 17, 5, 12]], - [[ -1, 1, 1],[-12,-17, 5]], - [[ -1, -1, -1],[ -5,-12, 17]], - [[ -1, 1, -1],[-17, -5, 12]], - [[ -1, -1, 1],[ 12,-17, -5]], - [[ -1, 1, -1],[ 5,-12,-17]], - [[ 1, 1, 1],[ 17, -5,-12]], - [[ 1, 1, 1],[-12, 17, -5]], - [[ 1, -1, -1],[ -5, 12,-17]], - [[ 1, 1, -1],[-17, 5,-12]], - ],dtype=float), - }, - 'NW': { - 'cF' : np.array([ - [[ 2, -1, -1],[ 1, 1, 1]], - [[ -1, 2, -1],[ 1, 1, 1]], - [[ -1, -1, 2],[ 1, 1, 1]], - [[ -2, -1, -1],[ -1, 1, 1]], - [[ 1, 2, -1],[ -1, 1, 1]], - [[ 1, -1, 2],[ -1, 1, 1]], - [[ 2, 1, -1],[ 1, -1, 1]], - [[ -1, -2, -1],[ 1, -1, 1]], - [[ -1, 1, 2],[ 1, -1, 1]], - [[ 2, -1, 1],[ -1, -1, 1]], - [[ -1, 2, 1],[ -1, -1, 1]], - [[ -1, -1, -2],[ -1, -1, 1]], - ],dtype=float), - 'cI' : np.array([ - [[ 0, -1, 1],[ 0, 1, 1]], - [[ 0, -1, 1],[ 0, 1, 1]], - [[ 0, -1, 1],[ 0, 1, 1]], - [[ 0, -1, 1],[ 0, 1, 1]], - [[ 0, -1, 1],[ 0, 1, 1]], - [[ 0, -1, 1],[ 0, 1, 1]], - [[ 0, -1, 1],[ 0, 1, 1]], - [[ 0, -1, 1],[ 0, 1, 1]], - [[ 0, -1, 1],[ 0, 1, 1]], - [[ 0, -1, 1],[ 0, 1, 1]], - [[ 0, -1, 1],[ 0, 1, 1]], - [[ 0, -1, 1],[ 0, 1, 1]], - ],dtype=float), - }, - 'Pitsch': { - 'cF' : np.array([ - [[ 1, 0, 1],[ 0, 1, 0]], - [[ 1, 1, 0],[ 0, 0, 1]], - [[ 0, 1, 1],[ 1, 0, 0]], - [[ 0, 1, -1],[ 1, 0, 0]], - [[ -1, 0, 1],[ 0, 1, 0]], - [[ 1, -1, 0],[ 0, 0, 1]], - [[ 1, 0, -1],[ 0, 1, 0]], - [[ -1, 1, 0],[ 0, 0, 1]], - [[ 0, -1, 1],[ 1, 0, 0]], - [[ 0, 1, 1],[ 1, 0, 0]], - [[ 1, 0, 1],[ 0, 1, 0]], - [[ 1, 1, 0],[ 0, 0, 1]], - ],dtype=float), - 'cI' : np.array([ - [[ 1, -1, 1],[ -1, 0, 1]], - [[ 1, 1, -1],[ 1, -1, 0]], - [[ -1, 1, 1],[ 0, 1, -1]], - [[ -1, 1, -1],[ 0, -1, -1]], - [[ -1, -1, 1],[ -1, 0, -1]], - [[ 1, -1, -1],[ -1, -1, 0]], - [[ 1, -1, -1],[ -1, 0, -1]], - [[ -1, 1, -1],[ -1, -1, 0]], - [[ -1, -1, 1],[ 0, -1, -1]], - [[ -1, 1, 1],[ 0, -1, 1]], - [[ 1, -1, 1],[ 1, 0, -1]], - [[ 1, 1, -1],[ -1, 1, 0]], - ],dtype=float), - }, - 'Bain': { - 'cF' : np.array([ - [[ 0, 1, 0],[ 1, 0, 0]], - [[ 0, 0, 1],[ 0, 1, 0]], - [[ 1, 0, 0],[ 0, 0, 1]], - ],dtype=float), - 'cI' : np.array([ - [[ 0, 1, 1],[ 1, 0, 0]], - [[ 1, 0, 1],[ 0, 1, 0]], - [[ 1, 1, 0],[ 0, 0, 1]], - ],dtype=float), - }, - 'Burgers' : { - 'cI' : np.array([ - [[ -1, 1, 1],[ 1, 1, 0]], - [[ -1, 1, -1],[ 1, 1, 0]], - [[ 1, 1, 1],[ 1, -1, 0]], - [[ 1, 1, -1],[ 1, -1, 0]], + 'KS': { # https://doi.org/10.1016/j.jallcom.2012.02.004 + 'cF-->cI' : [ + np.repeat(np.array([ + [[-1, 0, 1],[ 1, 1, 1]], + [[ 0, 1,-1],[ 1, 1, 1]], + [[ 1,-1, 0],[ 1, 1, 1]], - [[ 1, 1, -1],[ 1, 0, 1]], - [[ -1, 1, 1],[ 1, 0, 1]], - [[ 1, 1, 1],[ -1, 0, 1]], - [[ 1, -1, 1],[ -1, 0, 1]], + [[ 1, 0,-1],[ 1,-1, 1]], + [[-1,-1, 0],[ 1,-1, 1]], + [[ 0, 1, 1],[ 1,-1, 1]], - [[ -1, 1, -1],[ 0, 1, 1]], - [[ 1, 1, -1],[ 0, 1, 1]], - [[ -1, 1, 1],[ 0, -1, 1]], - [[ 1, 1, 1],[ 0, -1, 1]], - ],dtype=float), - 'hP' : np.array([ - [[ -1, 2, -1, 0],[ 0, 0, 0, 1]], - [[ -1, -1, 2, 0],[ 0, 0, 0, 1]], - [[ -1, 2, -1, 0],[ 0, 0, 0, 1]], - [[ -1, -1, 2, 0],[ 0, 0, 0, 1]], + [[ 0,-1, 1],[-1, 1, 1]], + [[-1, 0,-1],[-1, 1, 1]], + [[ 1, 1, 0],[-1, 1, 1]], - [[ -1, 2, -1, 0],[ 0, 0, 0, 1]], - [[ -1, -1, 2, 0],[ 0, 0, 0, 1]], - [[ -1, 2, -1, 0],[ 0, 0, 0, 1]], - [[ -1, -1, 2, 0],[ 0, 0, 0, 1]], + [[-1, 1, 0],[ 1, 1,-1]], + [[ 0,-1,-1],[ 1, 1,-1]], + [[ 1, 0, 1],[ 1, 1,-1]], + ],dtype=float), + 2,axis=0), + np.tile(np.array([[[ 1, 1,-1],[ 0, 1, 1]], + [[-1, 1,-1],[ 0, 1, 1]]],dtype=float), + (12,1,1)), + ], + 'cI-->cF' : [ + np.repeat(np.array([ + [[ 1, 1,-1],[ 0, 1, 1]], + [[ 1,-1, 1],[ 0, 1, 1]], - [[ -1, 2, -1, 0],[ 0, 0, 0, 1]], - [[ -1, -1, 2, 0],[ 0, 0, 0, 1]], - [[ -1, 2, -1, 0],[ 0, 0, 0, 1]], - [[ -1, -1, 2, 0],[ 0, 0, 0, 1]], - ],dtype=float), - }, - } + [[ 1, 1, 1],[ 0, 1,-1]], + [[-1, 1, 1],[ 0, 1,-1]], + + [[ 1, 1,-1],[ 1, 0, 1]], + [[ 1,-1,-1],[ 1, 0, 1]], + + [[ 1, 1, 1],[ 1, 0,-1]], + [[ 1,-1, 1],[ 1, 0,-1]], + + [[ 1,-1, 1],[ 1, 1, 0]], + [[ 1,-1,-1],[ 1, 1, 0]], + + [[ 1, 1, 1],[ 1,-1, 0]], + [[ 1, 1,-1],[ 1,-1, 0]], + ],dtype=float), + 2,axis=0), + np.tile(np.array([[[ 0, 1,-1],[ 1, 1, 1]], + [[ 0,-1, 1],[ 1, 1, 1]]],dtype=float), + (12,1,1)), + ], + }, + 'GT': { # https://doi.org/10.1107/S0021889805038276 + 'cF-->cI' : [ + np.array([ + [[ -5,-12, 17],[ 1, 1, 1]], + [[ 17, -5,-12],[ 1, 1, 1]], + [[-12, 17, -5],[ 1, 1, 1]], + [[ 5, 12, 17],[ -1, -1, 1]], + [[-17, 5,-12],[ -1, -1, 1]], + [[ 12,-17, -5],[ -1, -1, 1]], + [[ -5, 12,-17],[ -1, 1, 1]], + [[ 17, 5, 12],[ -1, 1, 1]], + [[-12,-17, 5],[ -1, 1, 1]], + [[ 5,-12,-17],[ 1, -1, 1]], + [[-17, -5, 12],[ 1, -1, 1]], + [[ 12, 17, 5],[ 1, -1, 1]], + [[ -5, 17,-12],[ 1, 1, 1]], + [[-12, -5, 17],[ 1, 1, 1]], + [[ 17,-12, -5],[ 1, 1, 1]], + [[ 5,-17,-12],[ -1, -1, 1]], + [[ 12, 5, 17],[ -1, -1, 1]], + [[-17, 12, -5],[ -1, -1, 1]], + [[ -5,-17, 12],[ -1, 1, 1]], + [[-12, 5,-17],[ -1, 1, 1]], + [[ 17, 12, 5],[ -1, 1, 1]], + [[ 5, 17, 12],[ 1, -1, 1]], + [[ 12, -5,-17],[ 1, -1, 1]], + [[-17,-12, 5],[ 1, -1, 1]], + ],dtype=float), + np.array([ + [[-17, -7, 17],[ 1, 0, 1]], + [[ 17,-17, -7],[ 1, 1, 0]], + [[ -7, 17,-17],[ 0, 1, 1]], + [[ 17, 7, 17],[ -1, 0, 1]], + [[-17, 17, -7],[ -1, -1, 0]], + [[ 7,-17,-17],[ 0, -1, 1]], + [[-17, 7,-17],[ -1, 0, 1]], + [[ 17, 17, 7],[ -1, 1, 0]], + [[ -7,-17, 17],[ 0, 1, 1]], + [[ 17, -7,-17],[ 1, 0, 1]], + [[-17,-17, 7],[ 1, -1, 0]], + [[ 7, 17, 17],[ 0, -1, 1]], + [[-17, 17, -7],[ 1, 1, 0]], + [[ -7,-17, 17],[ 0, 1, 1]], + [[ 17, -7,-17],[ 1, 0, 1]], + [[ 17,-17, -7],[ -1, -1, 0]], + [[ 7, 17, 17],[ 0, -1, 1]], + [[-17, 7,-17],[ -1, 0, 1]], + [[-17,-17, 7],[ -1, 1, 0]], + [[ -7, 17,-17],[ 0, 1, 1]], + [[ 17, 7, 17],[ -1, 0, 1]], + [[ 17, 17, 7],[ 1, -1, 0]], + [[ 7,-17,-17],[ 0, -1, 1]], + [[-17, -7, 17],[ 1, 0, 1]], + ],dtype=float), + ], + 'cI-->cF' : [ + np.array([ + [[-17, -7, 17],[ 1, 0, 1]], + [[ 17,-17, -7],[ 1, 1, 0]], + [[ -7, 17,-17],[ 0, 1, 1]], + [[ 17, 7, 17],[ -1, 0, 1]], + [[-17, 17, -7],[ -1, -1, 0]], + [[ 7,-17,-17],[ 0, -1, 1]], + [[-17, 7,-17],[ -1, 0, 1]], + [[ 17, 17, 7],[ -1, 1, 0]], + [[ -7,-17, 17],[ 0, 1, 1]], + [[ 17, -7,-17],[ 1, 0, 1]], + [[-17,-17, 7],[ 1, -1, 0]], + [[ 7, 17, 17],[ 0, -1, 1]], + [[-17, 17, -7],[ 1, 1, 0]], + [[ -7,-17, 17],[ 0, 1, 1]], + [[ 17, -7,-17],[ 1, 0, 1]], + [[ 17,-17, -7],[ -1, -1, 0]], + [[ 7, 17, 17],[ 0, -1, 1]], + [[-17, 7,-17],[ -1, 0, 1]], + [[-17,-17, 7],[ -1, 1, 0]], + [[ -7, 17,-17],[ 0, 1, 1]], + [[ 17, 7, 17],[ -1, 0, 1]], + [[ 17, 17, 7],[ 1, -1, 0]], + [[ 7,-17,-17],[ 0, -1, 1]], + [[-17, -7, 17],[ 1, 0, 1]], + ],dtype=float), + np.array([ + [[ -5,-12, 17],[ 1, 1, 1]], + [[ 17, -5,-12],[ 1, 1, 1]], + [[-12, 17, -5],[ 1, 1, 1]], + [[ 5, 12, 17],[ -1, -1, 1]], + [[-17, 5,-12],[ -1, -1, 1]], + [[ 12,-17, -5],[ -1, -1, 1]], + [[ -5, 12,-17],[ -1, 1, 1]], + [[ 17, 5, 12],[ -1, 1, 1]], + [[-12,-17, 5],[ -1, 1, 1]], + [[ 5,-12,-17],[ 1, -1, 1]], + [[-17, -5, 12],[ 1, -1, 1]], + [[ 12, 17, 5],[ 1, -1, 1]], + [[ -5, 17,-12],[ 1, 1, 1]], + [[-12, -5, 17],[ 1, 1, 1]], + [[ 17,-12, -5],[ 1, 1, 1]], + [[ 5,-17,-12],[ -1, -1, 1]], + [[ 12, 5, 17],[ -1, -1, 1]], + [[-17, 12, -5],[ -1, -1, 1]], + [[ -5,-17, 12],[ -1, 1, 1]], + [[-12, 5,-17],[ -1, 1, 1]], + [[ 17, 12, 5],[ -1, 1, 1]], + [[ 5, 17, 12],[ 1, -1, 1]], + [[ 12, -5,-17],[ 1, -1, 1]], + [[-17,-12, 5],[ 1, -1, 1]], + ],dtype=float), + ], + }, + 'GT_prime': { # https://doi.org/10.1107/S0021889805038276 + 'cF-->cI' : [ + np.array([ + [[ 0, 1, -1],[ 7, 17, 17]], + [[ -1, 0, 1],[ 17, 7, 17]], + [[ 1, -1, 0],[ 17, 17, 7]], + [[ 0, -1, -1],[ -7,-17, 17]], + [[ 1, 0, 1],[-17, -7, 17]], + [[ 1, -1, 0],[-17,-17, 7]], + [[ 0, 1, -1],[ 7,-17,-17]], + [[ 1, 0, 1],[ 17, -7,-17]], + [[ -1, -1, 0],[ 17,-17, -7]], + [[ 0, -1, -1],[ -7, 17,-17]], + [[ -1, 0, 1],[-17, 7,-17]], + [[ -1, -1, 0],[-17, 17, -7]], + [[ 0, -1, 1],[ 7, 17, 17]], + [[ 1, 0, -1],[ 17, 7, 17]], + [[ -1, 1, 0],[ 17, 17, 7]], + [[ 0, 1, 1],[ -7,-17, 17]], + [[ -1, 0, -1],[-17, -7, 17]], + [[ -1, 1, 0],[-17,-17, 7]], + [[ 0, -1, 1],[ 7,-17,-17]], + [[ -1, 0, -1],[ 17, -7,-17]], + [[ 1, 1, 0],[ 17,-17, -7]], + [[ 0, 1, 1],[ -7, 17,-17]], + [[ 1, 0, -1],[-17, 7,-17]], + [[ 1, 1, 0],[-17, 17, -7]], + ],dtype=float), + np.array([ + [[ 1, 1, -1],[ 12, 5, 17]], + [[ -1, 1, 1],[ 17, 12, 5]], + [[ 1, -1, 1],[ 5, 17, 12]], + [[ -1, -1, -1],[-12, -5, 17]], + [[ 1, -1, 1],[-17,-12, 5]], + [[ 1, -1, -1],[ -5,-17, 12]], + [[ -1, 1, -1],[ 12, -5,-17]], + [[ 1, 1, 1],[ 17,-12, -5]], + [[ -1, -1, 1],[ 5,-17,-12]], + [[ 1, -1, -1],[-12, 5,-17]], + [[ -1, -1, 1],[-17, 12, -5]], + [[ -1, -1, -1],[ -5, 17,-12]], + [[ 1, -1, 1],[ 12, 17, 5]], + [[ 1, 1, -1],[ 5, 12, 17]], + [[ -1, 1, 1],[ 17, 5, 12]], + [[ -1, 1, 1],[-12,-17, 5]], + [[ -1, -1, -1],[ -5,-12, 17]], + [[ -1, 1, -1],[-17, -5, 12]], + [[ -1, -1, 1],[ 12,-17, -5]], + [[ -1, 1, -1],[ 5,-12,-17]], + [[ 1, 1, 1],[ 17, -5,-12]], + [[ 1, 1, 1],[-12, 17, -5]], + [[ 1, -1, -1],[ -5, 12,-17]], + [[ 1, 1, -1],[-17, 5,-12]], + ],dtype=float), + ], + 'cI-->cF' : [ + np.array([ + [[ 1, 1, -1],[ 12, 5, 17]], + [[ -1, 1, 1],[ 17, 12, 5]], + [[ 1, -1, 1],[ 5, 17, 12]], + [[ -1, -1, -1],[-12, -5, 17]], + [[ 1, -1, 1],[-17,-12, 5]], + [[ 1, -1, -1],[ -5,-17, 12]], + [[ -1, 1, -1],[ 12, -5,-17]], + [[ 1, 1, 1],[ 17,-12, -5]], + [[ -1, -1, 1],[ 5,-17,-12]], + [[ 1, -1, -1],[-12, 5,-17]], + [[ -1, -1, 1],[-17, 12, -5]], + [[ -1, -1, -1],[ -5, 17,-12]], + [[ 1, -1, 1],[ 12, 17, 5]], + [[ 1, 1, -1],[ 5, 12, 17]], + [[ -1, 1, 1],[ 17, 5, 12]], + [[ -1, 1, 1],[-12,-17, 5]], + [[ -1, -1, -1],[ -5,-12, 17]], + [[ -1, 1, -1],[-17, -5, 12]], + [[ -1, -1, 1],[ 12,-17, -5]], + [[ -1, 1, -1],[ 5,-12,-17]], + [[ 1, 1, 1],[ 17, -5,-12]], + [[ 1, 1, 1],[-12, 17, -5]], + [[ 1, -1, -1],[ -5, 12,-17]], + [[ 1, 1, -1],[-17, 5,-12]], + ],dtype=float), + np.array([ + [[ 0, 1, -1],[ 7, 17, 17]], + [[ -1, 0, 1],[ 17, 7, 17]], + [[ 1, -1, 0],[ 17, 17, 7]], + [[ 0, -1, -1],[ -7,-17, 17]], + [[ 1, 0, 1],[-17, -7, 17]], + [[ 1, -1, 0],[-17,-17, 7]], + [[ 0, 1, -1],[ 7,-17,-17]], + [[ 1, 0, 1],[ 17, -7,-17]], + [[ -1, -1, 0],[ 17,-17, -7]], + [[ 0, -1, -1],[ -7, 17,-17]], + [[ -1, 0, 1],[-17, 7,-17]], + [[ -1, -1, 0],[-17, 17, -7]], + [[ 0, -1, 1],[ 7, 17, 17]], + [[ 1, 0, -1],[ 17, 7, 17]], + [[ -1, 1, 0],[ 17, 17, 7]], + [[ 0, 1, 1],[ -7,-17, 17]], + [[ -1, 0, -1],[-17, -7, 17]], + [[ -1, 1, 0],[-17,-17, 7]], + [[ 0, -1, 1],[ 7,-17,-17]], + [[ -1, 0, -1],[ 17, -7,-17]], + [[ 1, 1, 0],[ 17,-17, -7]], + [[ 0, 1, 1],[ -7, 17,-17]], + [[ 1, 0, -1],[-17, 7,-17]], + [[ 1, 1, 0],[-17, 17, -7]], + ],dtype=float), + ], + }, + 'NW': { # https://doi.org/10.1016/j.matchar.2004.12.015 + 'cF-->cI' : [ + np.array([ + [[ 2,-1,-1],[ 1, 1, 1]], + [[-1, 2,-1],[ 1, 1, 1]], + [[-1,-1, 2],[ 1, 1, 1]], + + [[-2,-1,-1],[-1, 1, 1]], + [[ 1, 2,-1],[-1, 1, 1]], + [[ 1,-1, 2],[-1, 1, 1]], + + [[ 2, 1,-1],[ 1,-1, 1]], + [[-1,-2,-1],[ 1,-1, 1]], + [[-1, 1, 2],[ 1,-1, 1]], + + [[ 2,-1, 1],[ 1, 1,-1]], + [[-1, 2, 1],[ 1, 1,-1]], + [[-1,-1,-2],[ 1, 1,-1]], + ],dtype=float), + np.broadcast_to(np.array([[ 0,-1, 1],[ 0, 1, 1]],dtype=float), + (12,2,3)), + ], + 'cI-->cF' : [ + np.repeat(np.array([ + [[ 0, 1,-1],[ 0, 1, 1]], + [[ 0, 1, 1],[ 0, 1,-1]], + [[ 1, 0,-1],[ 1, 0, 1]], + [[ 1, 0, 1],[ 1, 0,-1]], + [[ 1,-1, 0],[ 1, 1, 0]], + [[ 1, 1, 0],[ 1,-1, 0]], + ],dtype=float), + 2,axis=0), + np.tile(np.array([ + [[ 2,-1,-1],[ 1, 1, 1]], + [[-2, 1, 1],[ 1, 1, 1]], + ],dtype=float), + (6,1,1)), + ], + }, + 'Pitsch': { + 'cF-->cI' : [ + np.repeat(np.array([ + [[ 0, 1, 1],[ 1, 0, 0]], + [[ 0, 1,-1],[ 1, 0, 0]], + [[ 1, 0, 1],[ 0, 1, 0]], + [[ 1, 0,-1],[ 0, 1, 0]], + [[ 1, 1, 0],[ 0, 0, 1]], + [[ 1,-1, 0],[ 0, 0, 1]], + ],dtype=float), + 2,axis=0), + np.tile(np.array([ + [[ 1, 1,-1],[ 0, 1, 1]], + [[-1, 1,-1],[ 0, 1, 1]], + ],dtype=float), + (6,1,1)), + ], + 'cI-->cF' : [ + np.array([ + [[ 1, 1,-1],[ 0, 1, 1]], + [[ 1,-1, 1],[ 0, 1, 1]], + [[ 1, 1, 1],[ 0, 1,-1]], + [[-1, 1, 1],[ 0, 1,-1]], + [[ 1, 1,-1],[ 1, 0, 1]], + [[ 1,-1,-1],[ 1, 0, 1]], + [[ 1, 1, 1],[ 1, 0,-1]], + [[ 1,-1, 1],[ 1, 0,-1]], + [[ 1,-1, 1],[ 1, 1, 0]], + [[ 1,-1,-1],[ 1, 1, 0]], + [[ 1, 1, 1],[ 1,-1, 0]], + [[ 1, 1,-1],[ 1,-1, 0]], + ],dtype=float), + np.broadcast_to(np.array([[ 1, 1, 0],[ 0, 0, 1]],dtype=float), + (12,2,3)), + ], + }, + 'Bain': { # https://doi.org/10.1107/S0021889805038276 + 'cF-->cI' : [ + np.array([ + [[ 0, 1, 0],[ 1, 0, 0]], + [[ 0, 0, 1],[ 0, 1, 0]], + [[ 1, 0, 0],[ 0, 0, 1]], + ],dtype=float), + np.broadcast_to(np.array([[ 1, 1, 0],[ 0, 0, 1]],dtype=float), + (3,2,3)), + ], + 'cI-->cF' : [ + np.array([ + [[ 0, 1, 1],[ 1, 0, 0]], + [[ 1, 0, 1],[ 0, 1, 0]], + [[ 1, 1, 0],[ 0, 0, 1]], + ],dtype=float), + np.broadcast_to(np.array([[ 1, 0, 0],[ 0, 0, 1]],dtype=float), + (3,2,3)), + ] + }, + 'Burgers' : { + 'cI-->hP' : [ + np.array([ + [[ 1, 1,-1],[ 0, 1, 1]], + [[ 1,-1, 1],[ 0, 1, 1]], + [[ 1, 1, 1],[ 0, 1,-1]], + [[-1, 1, 1],[ 0, 1,-1]], + [[ 1, 1,-1],[ 1, 0, 1]], + [[ 1,-1,-1],[ 1, 0, 1]], + [[ 1, 1, 1],[ 1, 0,-1]], + [[ 1,-1, 1],[ 1, 0,-1]], + [[ 1,-1, 1],[ 1, 1, 0]], + [[ 1,-1,-1],[ 1, 1, 0]], + [[ 1, 1, 1],[ 1,-1, 0]], + [[ 1, 1,-1],[ 1,-1, 0]], + ],dtype=float), + np.broadcast_to(np.array([[ 2,-1,-1, 0],[ 0, 0, 0, 1]],dtype=float), + (12,2,4)), + ], + 'hP-->cI' : [ + np.repeat(np.array([ + [[ 2,-1,-1, 0],[ 0, 0, 0, 1]], + [[-1, 2,-1, 0],[ 0, 0, 0, 1]], + [[-1,-1, 2, 0],[ 0, 0, 0, 1]], + ],dtype=float), + 2,axis=0), + np.tile(np.array([ + [[ 1, 1,-1],[ 0, 1, 1]], + [[-1, 1,-1],[ 0, 1, 1]], + ],dtype=float), + (3,1,1)), + ] + }, +} class Crystal(): """ @@ -478,7 +608,7 @@ class Crystal(): @property def orientation_relationships(self): """Return labels of orientation relationships.""" - return [k for k,v in orientation_relationships.items() if self.lattice in v] + return [k for k,v in orientation_relationships.items() if np.any([m.startswith(self.lattice) for m in v])] @property @@ -753,7 +883,7 @@ class Crystal(): Crystal frame vector (reciprocal space) of Titanium along (1,0,0) plane normal: >>> import damask - >>> Ti = damask.Crystal(lattice='hP', a=0.295e-9, c=0.469e-9) + >>> Ti = damask.Crystal(lattice='hP', a=295e-12, c=469e-12) >>> Ti.to_frame(hkl=(1, 0, 0)) array([ 3.38983051e+09, 1.95711956e+09, -4.15134508e-07]) @@ -1025,7 +1155,8 @@ class Crystal(): def relation_operations(self, - model: str) -> Tuple[BravaisLattice, Rotation]: + model: str, + target = None) -> Tuple[BravaisLattice, Rotation]: """ Crystallographic orientation relationships for phase transformations. @@ -1033,6 +1164,10 @@ class Crystal(): ---------- model : str Name of orientation relationship. + target : Crystal + Crystal to transform to. + Providing this parameter allows specification of non-standard lattice parameters. + Defaults to standard parameters of target lattice. Returns ------- @@ -1057,20 +1192,21 @@ class Crystal(): https://doi.org/10.1016/j.actamat.2004.11.021 """ - my_relationships = {k:v for k,v in orientation_relationships.items() if self.lattice in v} - if model not in my_relationships: + if model not in self.orientation_relationships: raise KeyError(f'unknown orientation relationship "{model}"') - r = my_relationships[model] - sl = self.lattice - ol = (set(r)-{sl}).pop() - m = r[sl] - o = r[ol] + sep = '-->' + search = self.lattice+sep+('' if target is None else target.lattice) - p_,_p = np.zeros(m.shape[:-1]+(3,)),np.zeros(o.shape[:-1]+(3,)) - p_[...,0,:] = m[...,0,:] if m.shape[-1] == 3 else util.Bravais_to_Miller(uvtw=m[...,0,0:4]) - p_[...,1,:] = m[...,1,:] if m.shape[-1] == 3 else util.Bravais_to_Miller(hkil=m[...,1,0:4]) - _p[...,0,:] = o[...,0,:] if o.shape[-1] == 3 else util.Bravais_to_Miller(uvtw=o[...,0,0:4]) - _p[...,1,:] = o[...,1,:] if o.shape[-1] == 3 else util.Bravais_to_Miller(hkil=o[...,1,0:4]) + m_l,o_l = [transform.split(sep) for transform in orientation_relationships[model].keys() + if transform.startswith(search)][0] + m_p,o_p = orientation_relationships[model][m_l+sep+o_l] + other = Crystal(lattice=o_l) if target is None else target + m_p = np.stack((self.to_frame(uvw=m_p[:,0] if len(m_p[0,0])==3 else util.Bravais_to_Miller(uvtw=m_p[:,0])), + self.to_frame(hkl=m_p[:,1] if len(m_p[0,1])==3 else util.Bravais_to_Miller(hkil=m_p[:,1]))), + axis=1) + o_p = np.stack((other.to_frame(uvw=o_p[:,0] if len(o_p[0,0])==3 else util.Bravais_to_Miller(uvtw=o_p[:,0])), + other.to_frame(hkl=o_p[:,1] if len(o_p[0,1])==3 else util.Bravais_to_Miller(hkil=o_p[:,1]))), + axis=1) - return (ol,Rotation.from_parallel(p_,_p)) + return (o_l,Rotation.from_parallel(a=m_p,b=o_p)) diff --git a/python/damask/_orientation.py b/python/damask/_orientation.py index 7f5ea1cb5..a11494efe 100644 --- a/python/damask/_orientation.py +++ b/python/damask/_orientation.py @@ -240,13 +240,6 @@ class Orientation(Rotation,Crystal): return self.copy(Rotation(self.quaternion)*Rotation(other.quaternion)) - @classmethod - @util.extend_docstring(Rotation.from_random, - adopted_parameters=Crystal.__init__) - @util.pass_on('rotation', Rotation.from_random, wrapped=__init__) - def from_random(cls, **kwargs) -> 'Orientation': - return cls(**kwargs) - @classmethod @util.extend_docstring(Rotation.from_quaternion, adopted_parameters=Crystal.__init__) @@ -282,6 +275,13 @@ class Orientation(Rotation,Crystal): def from_matrix(cls, **kwargs) -> 'Orientation': return cls(**kwargs) + @classmethod + @util.extend_docstring(Rotation.from_parallel, + adopted_parameters=Crystal.__init__) + @util.pass_on('rotation', Rotation.from_parallel, wrapped=__init__) + def from_parallel(cls, **kwargs) -> 'Orientation': + return cls(**kwargs) + @classmethod @util.extend_docstring(Rotation.from_Rodrigues_vector, adopted_parameters=Crystal.__init__) @@ -303,6 +303,20 @@ class Orientation(Rotation,Crystal): def from_cubochoric(cls, **kwargs) -> 'Orientation': return cls(**kwargs) + @classmethod + @util.extend_docstring(Rotation.from_random, + adopted_parameters=Crystal.__init__) + @util.pass_on('rotation', Rotation.from_random, wrapped=__init__) + def from_random(cls, **kwargs) -> 'Orientation': + return cls(**kwargs) + + @classmethod + @util.extend_docstring(Rotation.from_ODF, + adopted_parameters=Crystal.__init__) + @util.pass_on('rotation', Rotation.from_ODF, wrapped=__init__) + def from_ODF(cls, **kwargs) -> 'Orientation': + return cls(**kwargs) + @classmethod @util.extend_docstring(Rotation.from_spherical_component, adopted_parameters=Crystal.__init__) @@ -325,7 +339,7 @@ class Orientation(Rotation,Crystal): hkl: FloatSequence, **kwargs) -> 'Orientation': """ - Initialize orientation object from two crystallographic directions. + Initialize orientation object from the crystallographic direction and plane parallel to lab x and z, respectively. Parameters ---------- @@ -855,7 +869,8 @@ class Orientation(Rotation,Crystal): def related(self: MyType, - model: str) -> MyType: + model: str, + target = None) -> MyType: """ All orientations related to self by given relationship model. @@ -863,6 +878,8 @@ class Orientation(Rotation,Crystal): ---------- model : str Orientation relationship model selected from self.orientation_relationships. + target : Crystal + Crystal to transform to. Returns ------- @@ -890,11 +907,10 @@ class Orientation(Rotation,Crystal): [0.924 0.000 0.000 0.383]] """ - lattice,o = self.relation_operations(model) - target = Crystal(lattice=lattice) - o = o.broadcast_to(o.shape+self.shape,mode='right') + lattice,o = self.relation_operations(model,target) + target = Crystal(lattice=lattice) if target is None else target - return Orientation(rotation=o*Rotation(self.quaternion).broadcast_to(o.shape,mode='left'), + return Orientation(rotation=o*Rotation(self.quaternion)[np.newaxis,...], lattice=lattice, b = self.b if target.ratio['b'] is None else self.a*target.ratio['b'], c = self.c if target.ratio['c'] is None else self.a*target.ratio['c'], diff --git a/python/tests/resources/Orientation/cF_Bain.txt b/python/tests/resources/Orientation/cF_Bain.txt index ae06eb2fb..e9b5d411d 100644 --- a/python/tests/resources/Orientation/cF_Bain.txt +++ b/python/tests/resources/Orientation/cF_Bain.txt @@ -1,4 +1,4 @@ 1_Eulers 2_Eulers 3_Eulers 1_pos 2_pos -180.0 45.00000000000001 180.0 1 1 -270.0 45.00000000000001 90.0 1 2 +90.0 90.0 315.0 1 1 +180.0 90.00000000000001 45.000000000000014 1 2 315.0 0.0 0.0 1 3 diff --git a/python/tests/resources/Orientation/cF_GT.txt b/python/tests/resources/Orientation/cF_GT.txt index e73885048..a15d13fcd 100644 --- a/python/tests/resources/Orientation/cF_GT.txt +++ b/python/tests/resources/Orientation/cF_GT.txt @@ -1,25 +1,25 @@ 1_Eulers 2_Eulers 3_Eulers 1_pos 2_pos -146.75362934444064 9.976439066337804 256.395594327347 1 1 -356.59977719102034 43.39784965440254 12.173896584899929 1 2 -75.92521636876346 43.82007387041961 277.8843642946069 1 3 -326.75362934444064 9.976439066337806 76.39559432734703 1 4 -176.59977719102034 43.397849654402556 192.17389658489986 1 5 -255.92521636876344 43.82007387041961 97.88436429460687 1 6 -213.24637065555936 9.976439066337804 103.604405672653 1 7 -3.400222808979685 43.39784965440255 347.8261034151001 1 8 +146.75362934444055 9.976439066337804 256.39559432734706 1 1 +356.59977719102034 43.39784965440254 12.173896584899923 1 2 +75.92521636876346 43.820073870419634 277.8843642946069 1 3 +326.7536293444406 9.976439066337804 76.39559432734708 1 4 +176.59977719102034 43.39784965440254 192.1738965848999 1 5 +255.92521636876344 43.82007387041961 97.88436429460688 1 6 +213.2463706555594 9.976439066337804 103.60440567265299 1 7 +3.4002228089796636 43.39784965440254 347.8261034151001 1 8 284.0747836312365 43.82007387041961 82.11563570539313 1 9 -33.24637065555936 9.976439066337804 283.60440567265294 1 10 -183.40022280897963 43.397849654402556 167.8261034151001 1 11 -104.07478363123654 43.82007387041961 262.1156357053931 1 12 -273.4002228089796 43.397849654402556 77.82610341510008 1 13 -123.24637065555939 9.976439066337806 193.60440567265297 1 14 -194.07478363123653 43.82007387041961 172.11563570539317 1 15 -93.40022280897969 43.39784965440255 257.8261034151001 1 16 -303.24637065555936 9.976439066337804 13.604405672652977 1 17 -14.074783631236542 43.82007387041961 352.1156357053931 1 18 -86.59977719102032 43.39784965440254 282.17389658489986 1 19 -236.75362934444058 9.976439066337804 166.39559432734703 1 20 -165.92521636876344 43.82007387041961 187.88436429460683 1 21 -266.59977719102034 43.39784965440254 102.17389658489992 1 22 -56.75362934444064 9.976439066337804 346.395594327347 1 23 -345.9252163687635 43.82007387041961 7.884364294606862 1 24 +33.246370655559474 9.976439066337804 283.6044056726529 1 10 +183.40022280897966 43.39784965440254 167.8261034151001 1 11 +104.07478363123657 43.82007387041961 262.1156357053931 1 12 +273.4002228089796 43.39784965440254 77.82610341510009 1 13 +123.24637065555936 9.976439066337804 193.60440567265303 1 14 +194.07478363123653 43.82007387041961 172.11563570539315 1 15 +93.40022280897966 43.39784965440256 257.82610341510014 1 16 +303.2463706555593 9.976439066337804 13.604405672653055 1 17 +14.07478363123655 43.82007387041961 352.1156357053931 1 18 +86.59977719102034 43.39784965440254 282.17389658489986 1 19 +236.75362934444064 9.976439066337804 166.39559432734697 1 20 +165.92521636876347 43.82007387041961 187.88436429460683 1 21 +266.59977719102034 43.39784965440254 102.17389658489991 1 22 +56.75362934444067 9.976439066337804 346.395594327347 1 23 +345.9252163687635 43.82007387041961 7.8843642946068595 1 24 diff --git a/python/tests/resources/Orientation/cF_GT_prime.txt b/python/tests/resources/Orientation/cF_GT_prime.txt index 3a9021912..964d8b858 100644 --- a/python/tests/resources/Orientation/cF_GT_prime.txt +++ b/python/tests/resources/Orientation/cF_GT_prime.txt @@ -1,25 +1,25 @@ 1_Eulers 2_Eulers 3_Eulers 1_pos 2_pos -166.39559432734697 9.976439066337804 236.75362934444058 1 1 -352.1156357053931 43.82007387041961 14.074783631236542 1 2 -77.82610341510008 43.397849654402556 273.4002228089796 1 3 -346.395594327347 9.976439066337804 56.75362934444064 1 4 -172.11563570539317 43.82007387041961 194.07478363123653 1 5 -257.8261034151001 43.39784965440255 93.40022280897969 1 6 -193.604405672653 9.976439066337804 123.24637065555939 1 7 -7.884364294606862 43.82007387041961 345.9252163687635 1 8 -282.17389658489986 43.39784965440254 86.59977719102032 1 9 -13.604405672652977 9.976439066337804 303.24637065555936 1 10 -187.88436429460683 43.82007387041961 165.92521636876344 1 11 -102.17389658489992 43.39784965440254 266.59977719102034 1 12 -277.8843642946069 43.82007387041961 75.92521636876346 1 13 -103.604405672653 9.976439066337804 213.24637065555936 1 14 -192.17389658489986 43.397849654402556 176.59977719102034 1 15 +166.39559432734703 9.976439066337804 236.75362934444064 1 1 +352.1156357053931 43.82007387041961 14.07478363123654 1 2 +77.82610341510009 43.39784965440254 273.4002228089796 1 3 +346.3955943273471 9.976439066337804 56.75362934444052 1 4 +172.11563570539315 43.82007387041959 194.07478363123653 1 5 +257.8261034151001 43.39784965440256 93.40022280897968 1 6 +193.60440567265294 9.976439066337804 123.24637065555943 1 7 +7.884364294606861 43.82007387041961 345.9252163687635 1 8 +282.17389658489986 43.39784965440254 86.59977719102034 1 9 +13.60440567265293 9.976439066337804 303.2463706555594 1 10 +187.88436429460683 43.82007387041961 165.92521636876347 1 11 +102.17389658489991 43.39784965440254 266.59977719102034 1 12 +277.8843642946069 43.82007387041961 75.92521636876347 1 13 +103.60440567265306 9.976439066337804 213.2463706555593 1 14 +192.1738965848999 43.39784965440254 176.59977719102034 1 15 97.88436429460687 43.82007387041961 255.92521636876344 1 16 -283.60440567265294 9.976439066337804 33.24637065555936 1 17 -12.173896584899929 43.39784965440254 356.59977719102034 1 18 -82.11563570539313 43.82007387041961 284.0747836312365 1 19 +283.60440567265294 9.976439066337804 33.24637065555943 1 17 +12.173896584899891 43.39784965440254 356.59977719102034 1 18 +82.11563570539315 43.82007387041961 284.0747836312365 1 19 256.395594327347 9.976439066337804 146.75362934444064 1 20 -167.8261034151001 43.397849654402556 183.40022280897963 1 21 -262.1156357053931 43.82007387041961 104.07478363123654 1 22 -76.39559432734703 9.976439066337806 326.75362934444064 1 23 -347.8261034151001 43.39784965440255 3.400222808979685 1 24 +167.8261034151001 43.39784965440254 183.40022280897966 1 21 +262.1156357053931 43.82007387041958 104.07478363123656 1 22 +76.39559432734696 9.976439066337804 326.7536293444407 1 23 +347.8261034151001 43.39784965440256 3.4002228089796644 1 24 diff --git a/python/tests/resources/Orientation/cF_KS.txt b/python/tests/resources/Orientation/cF_KS.txt index d63442cf3..8f8a9676b 100644 --- a/python/tests/resources/Orientation/cF_KS.txt +++ b/python/tests/resources/Orientation/cF_KS.txt @@ -1,25 +1,25 @@ 1_Eulers 2_Eulers 3_Eulers 1_pos 2_pos -114.20342833932975 10.52877936550932 204.20342833932972 1 1 -94.3573968784815 80.40593177313954 311.22729452432543 1 2 -175.6426031215185 80.40593177313954 48.77270547567447 1 3 -155.79657166067025 10.52877936550932 155.79657166067025 1 4 -99.62136089109411 85.70366403943004 318.04510841542015 1 5 -170.37863910890587 85.70366403943002 41.954891584579855 1 6 -85.64260312151852 80.40593177313954 48.77270547567448 1 7 -65.79657166067024 10.52877936550932 155.79657166067025 1 8 -9.621360891094124 85.70366403943004 318.04510841542015 1 9 -80.37863910890587 85.70366403943004 41.95489158457987 1 10 -24.203428339329758 10.52877936550932 204.20342833932975 1 11 -4.357396878481486 80.40593177313954 311.2272945243255 1 12 -204.20342833932972 10.52877936550932 204.20342833932972 1 13 -184.35739687848147 80.40593177313954 311.2272945243255 1 14 -265.64260312151845 80.40593177313953 48.77270547567449 1 15 -245.79657166067025 10.528779365509317 155.79657166067025 1 16 -189.62136089109413 85.70366403943004 318.04510841542015 1 17 -260.3786391089059 85.70366403943002 41.954891584579855 1 18 -170.37863910890587 94.29633596056996 138.04510841542015 1 19 -99.62136089109411 94.29633596056998 221.95489158457983 1 20 -155.79657166067025 169.4712206344907 24.203428339329754 1 21 +138.77270547567446 99.59406822686046 4.357396878481498 1 1 +94.35739687848151 80.40593177313954 311.2272945243255 1 2 +77.33353069348573 49.47122063449066 282.6664693065143 1 3 +155.79657166067022 10.528779365509285 155.79657166067028 1 4 +194.38500258182026 42.13367950584019 83.58843092115008 1 5 +170.37863910890584 85.70366403943002 41.95489158457988 1 6 +347.3335306934857 49.471220634490685 282.6664693065143 1 7 +65.79657166067024 10.528779365509285 155.79657166067025 1 8 +104.3850025818203 42.13367950584017 83.58843092115005 1 9 +80.37863910890589 85.70366403943004 41.95489158457986 1 10 +48.772705475674464 99.59406822686044 4.357396878481494 1 11 +4.357396878481504 80.40593177313954 311.22729452432554 1 12 +228.77270547567446 99.59406822686047 4.357396878481498 1 13 +184.35739687848152 80.40593177313954 311.2272945243255 1 14 +167.33353069348573 49.4712206344907 282.6664693065143 1 15 +245.79657166067025 10.528779365509285 155.79657166067025 1 16 +284.3850025818203 42.13367950584019 83.58843092115006 1 17 +260.3786391089059 85.70366403943002 41.95489158457986 1 18 +75.6149974181797 137.8663204941598 263.58843092115006 1 19 +99.62136089109411 94.29633596056996 221.95489158457985 1 20 +131.22729452432554 80.40593177313954 184.3573968784815 1 21 175.64260312151848 99.59406822686046 131.22729452432552 1 22 -94.35739687848151 99.59406822686046 228.77270547567446 1 23 -114.20342833932975 169.4712206344907 335.7965716606702 1 24 +192.66646930651427 130.52877936550937 102.66646930651426 1 23 +114.20342833932965 169.4712206344907 335.79657166067017 1 24 diff --git a/python/tests/resources/Orientation/cF_NW.txt b/python/tests/resources/Orientation/cF_NW.txt index d22b86e1b..3ffc546d4 100644 --- a/python/tests/resources/Orientation/cF_NW.txt +++ b/python/tests/resources/Orientation/cF_NW.txt @@ -1,13 +1,13 @@ 1_Eulers 2_Eulers 3_Eulers 1_pos 2_pos -96.91733794010702 83.13253115922213 314.5844440567886 1 1 -173.082662059893 83.13253115922211 45.41555594321143 1 2 -135.0 9.735610317245317 180.0 1 3 -263.082662059893 83.13253115922213 45.415555943211444 1 4 -186.91733794010702 83.13253115922211 314.5844440567886 1 5 -224.99999999999997 9.735610317245317 180.0 1 6 -83.082662059893 83.13253115922213 45.415555943211444 1 7 -6.917337940106983 83.13253115922211 314.5844440567886 1 8 -45.0 9.73561031724532 180.0 1 9 -13.638707279476469 45.81931182053557 80.40196970123216 1 10 -256.36129272052347 45.81931182053556 279.59803029876775 1 11 -315.0 99.73561031724536 0.0 1 12 +96.917337940107 83.13253115922213 314.5844440567886 1 1 +173.08266205989295 83.13253115922213 45.415555943211444 1 2 +135.00000000000003 9.735610317245321 179.99999999999997 1 3 +263.082662059893 83.13253115922213 45.41555594321144 1 4 +186.91733794010705 83.13253115922211 314.5844440567886 1 5 +224.99999999999997 9.735610317245396 180.00000000000003 1 6 +83.082662059893 83.13253115922213 45.41555594321142 1 7 +6.917337940107001 83.13253115922213 314.5844440567886 1 8 +45.000000000000036 9.735610317245321 179.99999999999994 1 9 +96.917337940107 96.86746884077787 225.41555594321142 1 10 +173.08266205989298 96.86746884077787 134.58444405678858 1 11 +135.0 170.26438968275468 0.0 1 12 diff --git a/python/tests/resources/Orientation/cF_Pitsch.txt b/python/tests/resources/Orientation/cF_Pitsch.txt index b18e5212b..078f07f75 100644 --- a/python/tests/resources/Orientation/cF_Pitsch.txt +++ b/python/tests/resources/Orientation/cF_Pitsch.txt @@ -1,13 +1,13 @@ 1_Eulers 2_Eulers 3_Eulers 1_pos 2_pos -135.41555594321144 83.13253115922213 173.082662059893 1 1 -260.26438968275465 90.0 135.0 1 2 -260.40196970123213 45.81931182053557 13.638707279476478 1 3 -314.5844440567886 83.13253115922213 96.91733794010702 1 4 -350.40196970123213 45.81931182053557 283.6387072794765 1 5 -170.26438968275465 90.0 224.99999999999997 1 6 -315.4155559432114 83.13253115922213 353.08266205989304 1 7 -99.73561031724536 90.0 225.0 1 8 -279.59803029876787 45.819311820535574 166.36129272052352 1 9 -134.58444405678856 83.13253115922213 276.91733794010696 1 10 -9.598030298767851 45.819311820535574 76.36129272052355 1 11 -9.735610317245369 90.0 315.0 1 12 +80.40196970123212 134.18068817946445 346.3612927205235 1 1 +45.41555594321142 96.86746884077787 276.91733794010696 1 2 +45.41555594321142 83.1325311592221 263.082662059893 1 3 +80.40196970123215 45.81931182053556 193.63870727947645 1 4 +224.58444405678856 96.86746884077789 83.08266205989298 1 5 +189.59803029876787 134.18068817946445 13.638707279476478 1 6 +189.59803029876787 45.81931182053558 166.36129272052355 1 7 +224.58444405678856 83.1325311592221 96.91733794010702 1 8 +350.26438968275465 44.999999999999986 0.0 1 9 +279.7356103172453 45.00000000000001 0.0 1 10 +260.26438968275465 44.999999999999986 0.0 1 11 +189.73561031724535 44.999999999999986 0.0 1 12 diff --git a/python/tests/resources/Orientation/cI_Bain.txt b/python/tests/resources/Orientation/cI_Bain.txt index 0bb55aaef..6ac70636b 100644 --- a/python/tests/resources/Orientation/cI_Bain.txt +++ b/python/tests/resources/Orientation/cI_Bain.txt @@ -1,4 +1,4 @@ 1_Eulers 2_Eulers 3_Eulers 1_pos 2_pos -0.0 45.00000000000001 0.0 1 1 -90.0 45.00000000000001 270.0 1 2 -45.00000000000001 0.0 0.0 1 3 +90.0 90.00000000000003 44.99999999999999 1 1 +180.0 89.99999999999999 135.0 1 2 +45.0 0.0 0.0 1 3 diff --git a/python/tests/resources/Orientation/cI_GT.txt b/python/tests/resources/Orientation/cI_GT.txt index c2805c3e4..8445b64d6 100644 --- a/python/tests/resources/Orientation/cI_GT.txt +++ b/python/tests/resources/Orientation/cI_GT.txt @@ -1,25 +1,25 @@ 1_Eulers 2_Eulers 3_Eulers 1_pos 2_pos -283.60440567265294 9.976439066337804 33.24637065555936 1 1 -167.8261034151001 43.397849654402556 183.40022280897963 1 2 -262.1156357053931 43.82007387041961 104.07478363123654 1 3 -103.604405672653 9.976439066337804 213.24637065555936 1 4 -347.8261034151001 43.39784965440255 3.400222808979685 1 5 +283.60440567265294 9.976439066337804 33.246370655559446 1 1 +167.8261034151001 43.39784965440254 183.40022280897966 1 2 +262.1156357053931 43.820073870419634 104.07478363123656 1 3 +103.60440567265293 9.976439066337804 213.24637065555945 1 4 +347.8261034151001 43.39784965440254 3.400222808979658 1 5 82.11563570539313 43.82007387041961 284.0747836312365 1 6 -76.39559432734703 9.976439066337806 326.75362934444064 1 7 -192.17389658489986 43.397849654402556 176.59977719102034 1 8 +76.39559432734703 9.976439066337804 326.7536293444406 1 7 +192.1738965848999 43.39784965440254 176.59977719102034 1 8 97.88436429460687 43.82007387041961 255.92521636876344 1 9 -256.395594327347 9.976439066337804 146.75362934444064 1 10 -12.173896584899929 43.39784965440254 356.59977719102034 1 11 -277.8843642946069 43.82007387041961 75.92521636876346 1 12 -102.17389658489992 43.39784965440254 266.59977719102034 1 13 -346.395594327347 9.976439066337804 56.75362934444064 1 14 -7.884364294606862 43.82007387041961 345.9252163687635 1 15 -282.17389658489986 43.39784965440254 86.59977719102032 1 16 -166.39559432734703 9.976439066337804 236.75362934444058 1 17 -187.88436429460683 43.82007387041961 165.92521636876344 1 18 -257.8261034151001 43.39784965440255 93.40022280897969 1 19 -13.604405672652977 9.976439066337804 303.24637065555936 1 20 -352.1156357053931 43.82007387041961 14.074783631236542 1 21 -77.82610341510008 43.397849654402556 273.4002228089796 1 22 -193.60440567265297 9.976439066337806 123.24637065555939 1 23 -172.11563570539317 43.82007387041961 194.07478363123653 1 24 +256.39559432734706 9.976439066337804 146.75362934444055 1 10 +12.173896584899904 43.39784965440254 356.59977719102034 1 11 +277.8843642946069 43.82007387041961 75.92521636876344 1 12 +102.17389658489991 43.39784965440254 266.59977719102034 1 13 +346.395594327347 9.976439066337804 56.75362934444066 1 14 +7.884364294606855 43.82007387041961 345.9252163687635 1 15 +282.17389658489986 43.39784965440256 86.59977719102035 1 16 +166.39559432734697 9.976439066337804 236.7536293444407 1 17 +187.88436429460685 43.82007387041961 165.92521636876344 1 18 +257.82610341510014 43.39784965440254 93.40022280897966 1 19 +13.60440567265301 9.976439066337804 303.24637065555936 1 20 +352.1156357053931 43.82007387041961 14.074783631236537 1 21 +77.82610341510009 43.39784965440254 273.4002228089796 1 22 +193.604405672653 9.976439066337804 123.24637065555933 1 23 +172.11563570539315 43.82007387041961 194.07478363123653 1 24 diff --git a/python/tests/resources/Orientation/cI_GT_prime.txt b/python/tests/resources/Orientation/cI_GT_prime.txt index 1d0f6c3c6..592b981b2 100644 --- a/python/tests/resources/Orientation/cI_GT_prime.txt +++ b/python/tests/resources/Orientation/cI_GT_prime.txt @@ -1,25 +1,25 @@ 1_Eulers 2_Eulers 3_Eulers 1_pos 2_pos -303.24637065555936 9.976439066337804 13.604405672652977 1 1 -165.92521636876344 43.82007387041961 187.88436429460683 1 2 +303.24637065555936 9.976439066337804 13.604405672652986 1 1 +165.92521636876347 43.82007387041961 187.88436429460683 1 2 266.59977719102034 43.39784965440254 102.17389658489992 1 3 -123.24637065555939 9.976439066337804 193.604405672653 1 4 -345.9252163687635 43.82007387041961 7.884364294606862 1 5 -86.59977719102032 43.39784965440254 282.17389658489986 1 6 -56.75362934444064 9.976439066337804 346.395594327347 1 7 -194.07478363123653 43.82007387041961 172.11563570539317 1 8 -93.40022280897969 43.39784965440255 257.8261034151001 1 9 -236.75362934444058 9.976439066337804 166.39559432734697 1 10 -14.074783631236542 43.82007387041961 352.1156357053931 1 11 -273.4002228089796 43.397849654402556 77.82610341510008 1 12 +123.2463706555595 9.976439066337804 193.60440567265286 1 4 +345.9252163687634 43.82007387041959 7.884364294606872 1 5 +86.59977719102034 43.39784965440256 282.17389658489986 1 6 +56.75362934444059 9.976439066337804 346.395594327347 1 7 +194.07478363123653 43.82007387041961 172.11563570539315 1 8 +93.40022280897968 43.39784965440254 257.8261034151001 1 9 +236.75362934444058 9.976439066337804 166.39559432734706 1 10 +14.074783631236523 43.82007387041961 352.1156357053931 1 11 +273.4002228089796 43.39784965440254 77.82610341510009 1 12 104.07478363123654 43.82007387041961 262.1156357053931 1 13 -326.75362934444064 9.976439066337806 76.39559432734703 1 14 -3.400222808979685 43.39784965440255 347.8261034151001 1 15 -284.0747836312365 43.82007387041961 82.11563570539313 1 16 -146.75362934444064 9.976439066337804 256.395594327347 1 17 -183.40022280897963 43.397849654402556 167.8261034151001 1 18 +326.7536293444407 9.976439066337804 76.39559432734696 1 14 +3.4002228089796604 43.39784965440254 347.8261034151001 1 15 +284.0747836312365 43.82007387041961 82.11563570539316 1 16 +146.75362934444055 9.976439066337804 256.39559432734706 1 17 +183.40022280897966 43.39784965440254 167.8261034151001 1 18 255.92521636876344 43.82007387041961 97.88436429460687 1 19 33.24637065555936 9.976439066337804 283.60440567265294 1 20 -356.59977719102034 43.39784965440254 12.173896584899929 1 21 -75.92521636876346 43.82007387041961 277.8843642946069 1 22 -213.24637065555936 9.976439066337804 103.604405672653 1 23 -176.59977719102034 43.397849654402556 192.17389658489986 1 24 +356.59977719102034 43.39784965440254 12.173896584899905 1 21 +75.92521636876346 43.82007387041958 277.8843642946069 1 22 +213.2463706555593 9.976439066337804 103.60440567265306 1 23 +176.59977719102034 43.39784965440256 192.1738965848999 1 24 diff --git a/python/tests/resources/Orientation/cI_KS.txt b/python/tests/resources/Orientation/cI_KS.txt index b35a07fb8..78e35298d 100644 --- a/python/tests/resources/Orientation/cI_KS.txt +++ b/python/tests/resources/Orientation/cI_KS.txt @@ -1,25 +1,25 @@ 1_Eulers 2_Eulers 3_Eulers 1_pos 2_pos -335.7965716606702 10.528779365509317 65.79657166067024 1 1 -228.77270547567446 80.40593177313953 85.64260312151849 1 2 -131.22729452432552 80.40593177313954 4.357396878481506 1 3 -24.20342833932977 10.52877936550932 24.20342833932976 1 4 -221.95489158457983 85.70366403943002 80.37863910890589 1 5 -138.04510841542015 85.70366403943004 9.621360891094124 1 6 -131.22729452432552 80.40593177313953 94.35739687848151 1 7 -24.203428339329765 10.52877936550932 114.20342833932976 1 8 -221.95489158457983 85.70366403943004 170.37863910890587 1 9 -138.04510841542015 85.70366403943004 99.62136089109411 1 10 -335.7965716606702 10.52877936550932 155.79657166067025 1 11 -228.77270547567448 80.40593177313954 175.6426031215185 1 12 -335.7965716606702 10.52877936550932 335.7965716606702 1 13 -228.77270547567448 80.40593177313954 355.6426031215185 1 14 -131.2272945243255 80.40593177313954 274.35739687848144 1 15 -24.203428339329747 10.52877936550932 294.2034283393298 1 16 -221.95489158457985 85.70366403943004 350.3786391089059 1 17 -138.04510841542015 85.70366403943004 279.6213608910941 1 18 -41.95489158457986 94.29633596056998 9.621360891094133 1 19 -318.04510841542015 94.29633596056996 80.37863910890589 1 20 -155.79657166067025 169.4712206344907 24.203428339329754 1 21 -48.77270547567448 99.59406822686046 4.357396878481504 1 22 -311.2272945243255 99.59406822686046 85.64260312151852 1 23 -204.20342833932975 169.4712206344907 65.79657166067024 1 24 +257.3335306934857 49.47122063449066 102.66646930651427 1 1 +131.22729452432554 80.40593177313957 4.357396878481523 1 2 +184.3573968784815 99.59406822686046 48.772705475674485 1 3 +24.20342833932965 10.528779365509285 24.20342833932986 1 4 +335.7965716606704 169.4712206344907 204.20342833932992 1 5 +175.6426031215185 80.40593177313954 228.77270547567446 1 6 +102.66646930651426 130.52877936550934 282.66646930651433 1 7 +228.77270547567448 99.59406822686046 184.35739687848152 1 8 +294.2034283393298 10.528779365509285 24.2034283393297 1 9 +94.35739687848152 99.59406822686047 48.772705475674485 1 10 +167.3335306934857 49.4712206344907 102.66646930651429 1 11 +41.22729452432552 80.40593177313954 4.3573968784814845 1 12 +12.666469306514255 130.5287793655093 282.6664693065143 1 13 +138.7727054756745 99.59406822686046 184.3573968784815 1 14 +245.79657166067028 169.4712206344907 204.20342833932978 1 15 +85.64260312151852 80.40593177313954 228.77270547567448 1 16 +165.61499741817968 137.86632049415985 83.58843092115008 1 17 +104.38500258182032 42.13367950584017 263.58843092115006 1 18 +189.62136089109413 85.70366403943004 138.04510841542015 1 19 +80.37863910890587 94.29633596056998 318.04510841542015 1 20 +350.3786391089059 94.29633596056996 318.04510841542015 1 21 +99.62136089109414 85.70366403943004 138.04510841542012 1 22 +14.385002581820302 42.13367950584017 263.5884309211501 1 23 +75.61499741817968 137.8663204941598 83.58843092115006 1 24 diff --git a/python/tests/resources/Orientation/cI_NW.txt b/python/tests/resources/Orientation/cI_NW.txt index b39889f28..52adf1891 100644 --- a/python/tests/resources/Orientation/cI_NW.txt +++ b/python/tests/resources/Orientation/cI_NW.txt @@ -1,13 +1,13 @@ 1_Eulers 2_Eulers 3_Eulers 1_pos 2_pos -225.41555594321144 83.13253115922213 83.08266205989301 1 1 -134.58444405678856 83.13253115922211 6.917337940107012 1 2 -4.702125169424418e-15 9.735610317245317 45.0 1 3 -134.58444405678856 83.13253115922213 276.91733794010696 1 4 -225.4155559432114 83.13253115922213 353.082662059893 1 5 -0.0 9.735610317245317 315.0 1 6 -134.58444405678858 83.13253115922213 96.91733794010702 1 7 -225.41555594321142 83.13253115922213 173.082662059893 1 8 -0.0 9.735610317245317 135.0 1 9 -99.59803029876785 45.81931182053557 166.36129272052355 1 10 -260.40196970123213 45.81931182053556 283.6387072794765 1 11 -180.0 99.73561031724535 225.0 1 12 +99.59803029876785 45.81931182053558 346.3612927205235 1 1 +225.41555594321144 83.13253115922213 83.082662059893 1 2 +260.40196970123213 134.18068817946443 166.36129272052355 1 3 +134.58444405678856 96.86746884077786 263.08266205989304 1 4 +9.598030298767839 45.81931182053556 346.3612927205236 1 5 +135.41555594321142 83.13253115922213 83.08266205989298 1 6 +170.40196970123213 134.18068817946443 166.36129272052355 1 7 +44.58444405678856 96.86746884077789 263.082662059893 1 8 +170.26438968275465 45.00000000000003 179.99999999999997 1 9 +99.73561031724535 135.0 0.0 1 10 +9.735610317245337 135.00000000000003 0.0 1 11 +80.26438968275465 45.00000000000001 179.99999999999997 1 12 diff --git a/python/tests/resources/Orientation/cI_Pitsch.txt b/python/tests/resources/Orientation/cI_Pitsch.txt index 6e0efddc6..9fa8e858a 100644 --- a/python/tests/resources/Orientation/cI_Pitsch.txt +++ b/python/tests/resources/Orientation/cI_Pitsch.txt @@ -1,13 +1,13 @@ 1_Eulers 2_Eulers 3_Eulers 1_pos 2_pos -6.9173379401070045 83.13253115922213 44.58444405678856 1 1 -45.0 89.99999999999999 279.7356103172453 1 2 -166.36129272052352 45.819311820535574 279.59803029876787 1 3 -83.08266205989301 83.13253115922213 225.41555594321144 1 4 -256.3612927205235 45.819311820535574 189.59803029876787 1 5 -315.0 90.0 9.735610317245369 1 6 -186.917337940107 83.13253115922213 224.58444405678856 1 7 -315.0 90.0 80.26438968275463 1 8 -13.638707279476478 45.81931182053557 260.40196970123213 1 9 -263.082662059893 83.13253115922213 45.415555943211444 1 10 -103.63870727947646 45.819311820535574 170.40196970123213 1 11 -224.99999999999997 90.0 170.26438968275465 1 12 +180.0 44.999999999999986 189.73561031724537 1 1 +180.0 44.999999999999986 80.26438968275463 1 2 +179.99999999999994 135.0 80.26438968275464 1 3 +180.0 135.0 9.735610317245355 1 4 +90.0 44.999999999999986 260.26438968275465 1 5 +90.00000000000001 45.00000000000001 189.73561031724532 1 6 +90.0 135.0 9.735610317245342 1 7 +90.00000000000001 135.0 80.26438968275467 1 8 +135.0 90.0 99.73561031724536 1 9 +135.0 90.0 170.26438968275463 1 10 +45.0 90.0 350.26438968275465 1 11 +45.00000000000001 89.99999999999999 279.7356103172453 1 12 diff --git a/python/tests/test_Orientation.py b/python/tests/test_Orientation.py index 6de2c99b1..2b802f12d 100644 --- a/python/tests/test_Orientation.py +++ b/python/tests/test_Orientation.py @@ -304,13 +304,6 @@ class TestOrientation: with pytest.raises(ValueError): eval(f'o.{function}(np.ones(4))') - @pytest.mark.parametrize('model',['Bain','KS','GT','GT_prime','NW','Pitsch']) - @pytest.mark.parametrize('lattice',['cF','cI']) - def test_relationship_forward_backward(self,model,lattice): - o = Orientation.from_random(lattice=lattice) - for i,r in enumerate(o.related(model)): - assert o.disorientation(r.related(model)[i]).as_axis_angle(degrees=True,pair=True)[1]<1.0e-5 - @pytest.mark.parametrize('model',['Bain','KS','GT','GT_prime','NW','Pitsch']) @pytest.mark.parametrize('lattice',['cF','cI']) def test_relationship_reference(self,update,res_path,model,lattice): From 926d86935f62ce757512509818c34b63975da632 Mon Sep 17 00:00:00 2001 From: Philip Eisenlohr Date: Tue, 24 Oct 2023 14:39:17 -0400 Subject: [PATCH 16/84] added typehinting --- python/damask/_crystal.py | 48 +++++++++++++++++++---------------- python/damask/_orientation.py | 4 +-- python/damask/_typehints.py | 4 +-- 3 files changed, 30 insertions(+), 26 deletions(-) diff --git a/python/damask/_crystal.py b/python/damask/_crystal.py index 12def3bda..5edc4c3f6 100644 --- a/python/damask/_crystal.py +++ b/python/damask/_crystal.py @@ -6,7 +6,8 @@ from ._typehints import FloatSequence, CrystalFamily, BravaisLattice, CrystalKin from . import util from . import Rotation -lattice_symmetries: Dict[BravaisLattice, CrystalFamily] = { + +lattice_symmetries: Dict[Optional[BravaisLattice], CrystalFamily] = { 'aP': 'triclinic', 'mP': 'monoclinic', @@ -27,7 +28,7 @@ lattice_symmetries: Dict[BravaisLattice, CrystalFamily] = { 'cF': 'cubic', } -orientation_relationships: Dict[str, Dict[BravaisLattice,np.ndarray]] = { +orientation_relationships: Dict[str, Dict[str,List[np.ndarray]]] = { 'KS': { # https://doi.org/10.1016/j.jallcom.2012.02.004 'cF-->cI' : [ np.repeat(np.array([ @@ -488,7 +489,7 @@ class Crystal(): if lattice is not None and family is not None and family != lattice_symmetries[lattice]: raise KeyError(f'incompatible family "{family}" for lattice "{lattice}"') - self.family = lattice_symmetries[lattice] if family is None else family + self.family = lattice_symmetries[lattice] if family is None else family self.lattice = lattice if self.lattice is not None: @@ -546,7 +547,7 @@ class Crystal(): def __eq__(self, - other: object) -> bool: + other): """ Return self==other. @@ -558,20 +559,20 @@ class Crystal(): Crystal to check for equality. """ - return NotImplemented if not isinstance(other, Crystal) else \ - self.lattice == other.lattice and \ - self.parameters == other.parameters and \ - self.family == other.family + return (NotImplemented if not isinstance(other, Crystal) else + self.lattice == other.lattice and + self.parameters == other.parameters and + self.family == other.family) @property - def parameters(self): + def parameters(self) -> Tuple: """Return lattice parameters a, b, c, alpha, beta, gamma.""" - if hasattr(self,'a'): return (self.a,self.b,self.c,self.alpha,self.beta,self.gamma) + return (self.a,self.b,self.c,self.alpha,self.beta,self.gamma) if hasattr(self,'a') else () @property - def immutable(self): + def immutable(self) -> Dict[str, float]: """Return immutable lattice parameters.""" - _immutable = { + _immutable: Dict[CrystalFamily, Dict[str,float]] = { 'cubic': { 'b': 1.0, 'c': 1.0, @@ -606,9 +607,9 @@ class Crystal(): @property - def orientation_relationships(self): + def orientation_relationships(self) -> List[str]: """Return labels of orientation relationships.""" - return [k for k,v in orientation_relationships.items() if np.any([m.startswith(self.lattice) for m in v])] + return [k for k,v in orientation_relationships.items() if np.any([m.startswith(str(self.lattice)) for m in v])] @property @@ -801,9 +802,9 @@ class Crystal(): @property - def lattice_points(self): + def lattice_points(self) -> np.ndarray: """Return lattice points.""" - _lattice_points = { + _lattice_points: Dict[str, List] = { 'P': [ ], 'S': [ @@ -824,8 +825,8 @@ class Crystal(): if self.lattice is None: raise KeyError('no lattice type specified') return np.array([[0,0,0]] - + _lattice_points.get(self.lattice if self.lattice == 'hP' else \ - self.lattice[-1],None),dtype=float) + + _lattice_points.get(self.lattice if self.lattice == 'hP' else + self.lattice[-1],[]),dtype=float) def to_lattice(self, *, direction: Optional[FloatSequence] = None, @@ -912,7 +913,7 @@ class Crystal(): Directions and planes of deformation mode families. """ - _kinematics: Dict[BravaisLattice, Dict[CrystalKinematics, List[np.ndarray]]] = { + _kinematics: Dict[Optional[BravaisLattice], Dict[CrystalKinematics, List[np.ndarray]]] = { 'cF': { 'slip': [np.array([ [ 0,+1,-1, +1,+1,+1], @@ -1192,14 +1193,17 @@ class Crystal(): https://doi.org/10.1016/j.actamat.2004.11.021 """ + m_l: BravaisLattice + o_l: BravaisLattice + if model not in self.orientation_relationships: raise KeyError(f'unknown orientation relationship "{model}"') sep = '-->' - search = self.lattice+sep+('' if target is None else target.lattice) - - m_l,o_l = [transform.split(sep) for transform in orientation_relationships[model].keys() + search = self.lattice+sep+('' if target is None else target.lattice) # type: ignore + m_l,o_l = [transform.split(sep) for transform in orientation_relationships[model].keys() # type: ignore if transform.startswith(search)][0] + m_p,o_p = orientation_relationships[model][m_l+sep+o_l] other = Crystal(lattice=o_l) if target is None else target m_p = np.stack((self.to_frame(uvw=m_p[:,0] if len(m_p[0,0])==3 else util.Bravais_to_Miller(uvtw=m_p[:,0])), diff --git a/python/damask/_orientation.py b/python/damask/_orientation.py index a11494efe..72e289859 100644 --- a/python/damask/_orientation.py +++ b/python/damask/_orientation.py @@ -122,7 +122,7 @@ class Orientation(Rotation,Crystal): def __eq__(self, - other: object) -> bool: + other: Union[object,MyType]) -> bool: """ Return self==other. @@ -910,7 +910,7 @@ class Orientation(Rotation,Crystal): lattice,o = self.relation_operations(model,target) target = Crystal(lattice=lattice) if target is None else target - return Orientation(rotation=o*Rotation(self.quaternion)[np.newaxis,...], + return Orientation(rotation=o*Rotation(self.quaternion)[np.newaxis,...], # type: ignore lattice=lattice, b = self.b if target.ratio['b'] is None else self.a*target.ratio['b'], c = self.c if target.ratio['c'] is None else self.a*target.ratio['c'], diff --git a/python/damask/_typehints.py b/python/damask/_typehints.py index 5dbd80cae..b9e953189 100644 --- a/python/damask/_typehints.py +++ b/python/damask/_typehints.py @@ -10,8 +10,8 @@ FloatSequence = Union[np.ndarray,Sequence[float]] IntSequence = Union[np.ndarray,Sequence[int]] StrSequence = Union[np.ndarray,Sequence[str]] FileHandle = Union[TextIO, str, Path] -CrystalFamily = Union[None,Literal['triclinic', 'monoclinic', 'orthorhombic', 'tetragonal', 'hexagonal', 'cubic']] -BravaisLattice = Union[None,Literal['aP', 'mP', 'mS', 'oP', 'oS', 'oI', 'oF', 'tP', 'tI', 'hP', 'cP', 'cI', 'cF']] +CrystalFamily = Literal['triclinic', 'monoclinic', 'orthorhombic', 'tetragonal', 'hexagonal', 'cubic'] +BravaisLattice = Literal['aP', 'mP', 'mS', 'oP', 'oS', 'oI', 'oF', 'tP', 'tI', 'hP', 'cP', 'cI', 'cF'] CrystalKinematics = Literal['slip', 'twin'] NumpyRngSeed = Union[int, IntSequence, np.random.SeedSequence, np.random.Generator] # BitGenerator does not exists in older numpy versions From 934835f93c135ffd918224bf278cc9efb20dce6d Mon Sep 17 00:00:00 2001 From: Philip Eisenlohr Date: Tue, 24 Oct 2023 15:00:19 -0400 Subject: [PATCH 17/84] proper return value when lacking lattice information --- python/damask/_crystal.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/python/damask/_crystal.py b/python/damask/_crystal.py index 5edc4c3f6..42dd5a4aa 100644 --- a/python/damask/_crystal.py +++ b/python/damask/_crystal.py @@ -565,9 +565,9 @@ class Crystal(): self.family == other.family) @property - def parameters(self) -> Tuple: + def parameters(self) -> Optional[Tuple]: """Return lattice parameters a, b, c, alpha, beta, gamma.""" - return (self.a,self.b,self.c,self.alpha,self.beta,self.gamma) if hasattr(self,'a') else () + return (self.a,self.b,self.c,self.alpha,self.beta,self.gamma) if hasattr(self,'a') else None @property def immutable(self) -> Dict[str, float]: From 80061fafd6e6f2c846c59210b09ae93616e9f46d Mon Sep 17 00:00:00 2001 From: Philip Eisenlohr Date: Fri, 27 Oct 2023 15:09:00 +0000 Subject: [PATCH 18/84] Update README.md with repository locations --- README.md | 24 ++++++++++++++++++------ 1 file changed, 18 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 5b5edf677..80cfea135 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,23 @@ # DAMASK - The Düsseldorf Advanced Material Simulation Kit -Visit [damask.mpie.de](https://damask.mpie.de) for installation and usage instructions +- Usage, installation, and support: https://damask.mpie.de +- Code development: https://git.damask.mpie.de +- General inquiries: damask@mpie.de + + +## Repository Locations + +### [git.damask.mpie.de](https://git.damask.mpie.de) + +All code development is centralized in the principal DAMASK code repository hosted at [git.damask.mpie.de](https://git.damask.mpie.de). +Access to this GitLab instance requires registration and is granted to anyone with an interest in actively supporting the development of DAMASK. + +### [github.com](https://github.com) + +GitHub hosts the publicly accessible, but read-only, mirror of the principal DAMASK code repository and replicates its three top-level branches from [git.damask.mpie.de](https://git.damask.mpie.de). + +The site is primarily meant to provide a forum for [Discussions](https://github.com/eisenforschung/DAMASK/discussions) and [Issues](https://github.com/eisenforschung/DAMASK/issues). + ## Contact Information @@ -8,8 +25,3 @@ Max-Planck-Institut für Eisenforschung GmbH Max-Planck-Str. 1 40237 Düsseldorf Germany - -damask@mpie.de -https://damask.mpie.de -https://git.damask.mpie.de - From f67660902c2713000cc9c003bd594172cf6b1868 Mon Sep 17 00:00:00 2001 From: Franz Roters Date: Fri, 27 Oct 2023 17:18:02 +0200 Subject: [PATCH 19/84] corrected and adapted script for Marc2023.x --- processing/mentat_spectralBox.py | 34 +++++++++++++++----------------- 1 file changed, 16 insertions(+), 18 deletions(-) diff --git a/processing/mentat_spectralBox.py b/processing/mentat_spectralBox.py index de6307469..0c0405ce9 100755 --- a/processing/mentat_spectralBox.py +++ b/processing/mentat_spectralBox.py @@ -65,13 +65,13 @@ def mesh(r,d): return [ "*add_nodes", "%f %f %f"%(0.0,0.0,0.0), - "%f %f %f"%(0.0,0.0,d[2]), - "%f %f %f"%(0.0,d[1],d[2]), + "%f %f %f"%(d[0],0.0,0.0), + "%f %f %f"%(d[0],d[1],0.0), "%f %f %f"%(0.0,d[1],0.0), - "%f %f %f"%(-d[0],0.0,0.0), - "%f %f %f"%(-d[0],0.0,d[2]), - "%f %f %f"%(-d[0],d[1],d[2]), - "%f %f %f"%(-d[0],d[1],0.0), + "%f %f %f"%(0.0,0.0,d[2]), + "%f %f %f"%(d[0],0.0,d[2]), + "%f %f %f"%(d[0],d[1],d[2]), + "%f %f %f"%(0.0,d[1],d[2]), "*add_elements", "1", "2", @@ -82,19 +82,17 @@ def mesh(r,d): "7", "8", "*sub_divisions", - "%i %i %i"%(r[2],r[1],r[0]), + "%i %i %i"%(r[0],r[1],r[2]), "*subdivide_elements", "all_existing", "*set_sweep_tolerance", "%f"%(float(min(d))/max(r)/2.0), "*sweep_all", - "*renumber_all", - "*set_move_scale_factor x -1", - "*move_elements", - "all_existing", - "*flip_elements", - "all_existing", - "*fill_view", + "*remove_unused_nodes", + "*set_renumber_direction", + "%i %i %i"%(1,r[0],r[0]*r[1]), + "*renumber_elements_directed", + "*renumber_nodes_directed", ] @@ -142,8 +140,7 @@ def initial_conditions(material): cmds = [\ "*new_icond", "*icond_name _temperature", - "*icond_type state_variable", - "*icond_param_value state_var_id 1", + "*icond_type elem_temperature_state", "*icond_dof_value var 300", "*add_icond_elements", "all_existing", @@ -153,9 +150,9 @@ def initial_conditions(material): cmds.append([\ "*new_icond", "*icond_name material_%i"%(grain+1), - "*icond_type state_variable", + "*icond_type elem_user_state", "*icond_param_value state_var_id 2", - "*icond_dof_value var %i"%(grain+1), + "*icond_dof_value var %i"%(grain), "*add_icond_elements", elementList, "#", @@ -209,6 +206,7 @@ for name in filenames: '*show_model', '*redraw', '*draw_automatic', + '*fill_view', ] output_locals = {} From 51526b6ae41781a20e9b86e09678c688016300ee Mon Sep 17 00:00:00 2001 From: Test User Date: Fri, 27 Oct 2023 19:56:50 +0200 Subject: [PATCH 20/84] [skip ci] updated version information after successful test of v3.0.0-alpha7-904-g2a00de5fb --- VERSION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/VERSION b/VERSION index 133c77787..8e999f891 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -3.0.0-alpha7-901-ga25d1b1b9 +3.0.0-alpha7-904-g2a00de5fb From 80c6823777acf81eb595c5998566af62fda5990d Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Sun, 29 Oct 2023 22:34:23 +0100 Subject: [PATCH 21/84] new temperature-dependent elastic constants --- PRIVATE | 2 +- .../mechanical/elastic/Hooke_W-10at.%Re.yaml | 21 +++++++++++++++++++ .../mechanical/elastic/Hooke_W-3at.%Re.yaml | 21 +++++++++++++++++++ 3 files changed, 43 insertions(+), 1 deletion(-) create mode 100644 examples/config/phase/mechanical/elastic/Hooke_W-10at.%Re.yaml create mode 100644 examples/config/phase/mechanical/elastic/Hooke_W-3at.%Re.yaml diff --git a/PRIVATE b/PRIVATE index 42b20feeb..2cfe059d4 160000 --- a/PRIVATE +++ b/PRIVATE @@ -1 +1 @@ -Subproject commit 42b20feeb44e71504f3ec757ced18c04e55804fe +Subproject commit 2cfe059d411b00e62e005107c3031ec988b2cd84 diff --git a/examples/config/phase/mechanical/elastic/Hooke_W-10at.%Re.yaml b/examples/config/phase/mechanical/elastic/Hooke_W-10at.%Re.yaml new file mode 100644 index 000000000..921f1e912 --- /dev/null +++ b/examples/config/phase/mechanical/elastic/Hooke_W-10at.%Re.yaml @@ -0,0 +1,21 @@ +type: Hooke + +references: + - R.A. Ayres et al., + Journal of Applied Physics 46:1526-1530, 1975, + https://doi.org/10.1063/1.321804, + fit to Table IV (T_min=100K, T_max=300K) + +C_11: 524.6e+9 +C_11,T: -5.783e+07 +C_11,T^2: -1.355e+05 + +C_12: 219.0e+9 +C_12,T: 1.940e+07 +C_12,T^2: 7.634e+04 + +C_44: 168.7e+9 +C_44,T: -2.048e+07 +C_44,T^2: -5.478e+04 + +T_ref: 293.15 diff --git a/examples/config/phase/mechanical/elastic/Hooke_W-3at.%Re.yaml b/examples/config/phase/mechanical/elastic/Hooke_W-3at.%Re.yaml new file mode 100644 index 000000000..000b7fb04 --- /dev/null +++ b/examples/config/phase/mechanical/elastic/Hooke_W-3at.%Re.yaml @@ -0,0 +1,21 @@ +type: Hooke + +references: + - R.A. Ayres et al., + Journal of Applied Physics 46:1526-1530, 1975, + https://doi.org/10.1063/1.321804, + fit to Table IV (T_min=100K, T_max=300K) + +C_11: 535.1e+9 +C_11,T: -6.459e+07 +C_11,T^2: -1.664e+05 + +C_12: 216.1e+9 +C_12,T: 2.357e+07 +C_12,T^2: 5.186e+04 + +C_44: 161.1e+9 +C_44,T: -1.498e+07 +C_44,T^2: -3.234e+04 + +T_ref: 293.15 From 5a5f1cfc5b9faded92a80439f5e1b45696562a5c Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Thu, 2 Nov 2023 15:41:20 +0100 Subject: [PATCH 22/84] mesh works again for PETSc >= 3.19 needs also bugfix in PETSc: https://gitlab.com/petsc/petsc/-/merge_requests/6986 --- src/mesh/FEM_utilities.f90 | 5 +++-- src/mesh/mesh_mech_FEM.f90 | 2 ++ 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/src/mesh/FEM_utilities.f90 b/src/mesh/FEM_utilities.f90 index 4480e412d..3af7b77ea 100644 --- a/src/mesh/FEM_utilities.f90 +++ b/src/mesh/FEM_utilities.f90 @@ -122,19 +122,20 @@ subroutine FEM_utilities_init(num_mesh) flush(IO_STDOUT) call PetscOptionsClear(PETSC_NULL_OPTIONS,err_PETSc) CHKERRQ(err_PETSc) - CHKERRQ(err_PETSc) petsc_options = misc_prefixOptions('-snes_type newtonls & &-snes_linesearch_type cp -snes_ksp_ew & &-snes_ksp_ew_rtol0 0.01 -snes_ksp_ew_rtolmax 0.01 & &-ksp_type fgmres -ksp_max_it 25 ' // & num_mech%get_asStr('PETSc_options',defaultVal=''), 'mechanical_') - write(petsc_optionsOrder,'(a,i0)') '-mechFE_petscspace_degree ', p_s petsc_options = petsc_options // ' ' // petsc_optionsOrder call PetscOptionsInsertString(PETSC_NULL_OPTIONS,petsc_options,err_PETSc) CHKERRQ(err_PETSc) + call PetscOptionsSetValue(PETSC_NULL_OPTIONS,'-petscds_force_quad','0',err_PETSc) + CHKERRQ(err_PETSc) + wgt = real(mesh_maxNips*mesh_NcpElemsGlobal,pREAL)**(-1) end subroutine FEM_utilities_init diff --git a/src/mesh/mesh_mech_FEM.f90 b/src/mesh/mesh_mech_FEM.f90 index 67b7859ba..2d5556e63 100644 --- a/src/mesh/mesh_mech_FEM.f90 +++ b/src/mesh/mesh_mech_FEM.f90 @@ -155,6 +155,8 @@ subroutine FEM_mechanical_init(fieldBC,num_mesh) CHKERRQ(err_PETSc) call DMGetDimension(mechanical_mesh,dimPlex,err_PETSc) CHKERRQ(err_PETSc) + call DMSetFromOptions(mechanical_mesh,err_PETSc) + CHKERRQ(err_PETSc) !-------------------------------------------------------------------------------------------------- ! Setup FEM mech discretization From bb234938cc14a31ec804da359cee6ef1495db003 Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Thu, 2 Nov 2023 15:43:11 +0100 Subject: [PATCH 23/84] whitespace polishing --- src/mesh/FEM_quadrature.f90 | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/mesh/FEM_quadrature.f90 b/src/mesh/FEM_quadrature.f90 index 891e0be0d..b618fad92 100644 --- a/src/mesh/FEM_quadrature.f90 +++ b/src/mesh/FEM_quadrature.f90 @@ -20,13 +20,13 @@ module FEM_quadrature -1.0_pREAL, 1.0_pREAL, -1.0_pREAL, & -1.0_pREAL, -1.0_pREAL, 1.0_pREAL], shape=[3,4]) - type :: group_real !< variable length datatype + type :: group_real !< variable length datatype real(pREAL), dimension(:), allocatable :: p end type group_real - integer, dimension(2:3,maxOrder), public, protected :: & + integer, dimension(2:3,maxOrder), public, protected :: & FEM_nQuadrature !< number of quadrature points for spatial dimension(2-3) and interpolation order (1-maxOrder) - type(group_real), dimension(2:3,maxOrder), public, protected :: & + type(group_real), dimension(2:3,maxOrder), public, protected :: & FEM_quadrature_weights, & !< quadrature weights for each quadrature rule FEM_quadrature_points !< quadrature point coordinates (in simplical system) for each quadrature rule From d38ab0e07a8adb1766963041f0c813f54c5fd8be Mon Sep 17 00:00:00 2001 From: Philip Eisenlohr Date: Fri, 3 Nov 2023 18:51:57 -0400 Subject: [PATCH 24/84] signed direction as in KS paper; added Pitsch and Burgers references --- python/damask/_crystal.py | 6 ++--- python/tests/resources/Orientation/cF_KS.txt | 24 ++++++++++---------- 2 files changed, 15 insertions(+), 15 deletions(-) diff --git a/python/damask/_crystal.py b/python/damask/_crystal.py index 42dd5a4aa..8718b79c5 100644 --- a/python/damask/_crystal.py +++ b/python/damask/_crystal.py @@ -49,7 +49,7 @@ orientation_relationships: Dict[str, Dict[str,List[np.ndarray]]] = { [[ 1, 0, 1],[ 1, 1,-1]], ],dtype=float), 2,axis=0), - np.tile(np.array([[[ 1, 1,-1],[ 0, 1, 1]], + np.tile(np.array([[[-1,-1, 1],[ 0, 1, 1]], [[-1, 1,-1],[ 0, 1, 1]]],dtype=float), (12,1,1)), ], @@ -338,7 +338,7 @@ orientation_relationships: Dict[str, Dict[str,List[np.ndarray]]] = { (6,1,1)), ], }, - 'Pitsch': { + 'Pitsch': { # https://doi.org/10.1080/14786435908238253 'cF-->cI' : [ np.repeat(np.array([ [[ 0, 1, 1],[ 1, 0, 0]], @@ -394,7 +394,7 @@ orientation_relationships: Dict[str, Dict[str,List[np.ndarray]]] = { (3,2,3)), ] }, - 'Burgers' : { + 'Burgers' : { # https://doi.org/10.1016/S0031-8914(34)80244-3 'cI-->hP' : [ np.array([ [[ 1, 1,-1],[ 0, 1, 1]], diff --git a/python/tests/resources/Orientation/cF_KS.txt b/python/tests/resources/Orientation/cF_KS.txt index 8f8a9676b..85a3624eb 100644 --- a/python/tests/resources/Orientation/cF_KS.txt +++ b/python/tests/resources/Orientation/cF_KS.txt @@ -1,25 +1,25 @@ 1_Eulers 2_Eulers 3_Eulers 1_pos 2_pos -138.77270547567446 99.59406822686046 4.357396878481498 1 1 +114.2034283393298 10.528779365509285 204.2034283393297 1 1 94.35739687848151 80.40593177313954 311.2272945243255 1 2 -77.33353069348573 49.47122063449066 282.6664693065143 1 3 +175.64260312151848 80.40593177313957 48.77270547567447 1 3 155.79657166067022 10.528779365509285 155.79657166067028 1 4 -194.38500258182026 42.13367950584019 83.58843092115008 1 5 +99.62136089109413 85.70366403943005 318.04510841542015 1 5 170.37863910890584 85.70366403943002 41.95489158457988 1 6 -347.3335306934857 49.471220634490685 282.6664693065143 1 7 +85.64260312151852 80.40593177313954 48.77270547567447 1 7 65.79657166067024 10.528779365509285 155.79657166067025 1 8 -104.3850025818203 42.13367950584017 83.58843092115005 1 9 +9.62136089109412 85.70366403943004 318.04510841542015 1 9 80.37863910890589 85.70366403943004 41.95489158457986 1 10 -48.772705475674464 99.59406822686044 4.357396878481494 1 11 +24.203428339329754 10.528779365509356 204.20342833932975 1 11 4.357396878481504 80.40593177313954 311.22729452432554 1 12 -228.77270547567446 99.59406822686047 4.357396878481498 1 13 +204.2034283393298 10.528779365509356 204.2034283393297 1 13 184.35739687848152 80.40593177313954 311.2272945243255 1 14 -167.33353069348573 49.4712206344907 282.6664693065143 1 15 +265.6426031215185 80.40593177313954 48.772705475674464 1 15 245.79657166067025 10.528779365509285 155.79657166067025 1 16 -284.3850025818203 42.13367950584019 83.58843092115006 1 17 +189.62136089109413 85.70366403943002 318.04510841542015 1 17 260.3786391089059 85.70366403943002 41.95489158457986 1 18 -75.6149974181797 137.8663204941598 263.58843092115006 1 19 +170.37863910890587 94.29633596056996 138.04510841542015 1 19 99.62136089109411 94.29633596056996 221.95489158457985 1 20 -131.22729452432554 80.40593177313954 184.3573968784815 1 21 +155.79657166067025 169.47122063449072 24.203428339329754 1 21 175.64260312151848 99.59406822686046 131.22729452432552 1 22 -192.66646930651427 130.52877936550937 102.66646930651426 1 23 +94.35739687848148 99.59406822686044 228.77270547567446 1 23 114.20342833932965 169.4712206344907 335.79657166067017 1 24 From 50d47908fbe7e8a73ae4b622741fe6a72aafccfb Mon Sep 17 00:00:00 2001 From: Philip Eisenlohr Date: Mon, 6 Nov 2023 10:00:07 -0500 Subject: [PATCH 25/84] clearer help --- python/damask/_crystal.py | 4 ++-- python/damask/_orientation.py | 4 +++- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/python/damask/_crystal.py b/python/damask/_crystal.py index 8718b79c5..f5034a4a4 100644 --- a/python/damask/_crystal.py +++ b/python/damask/_crystal.py @@ -1165,10 +1165,10 @@ class Crystal(): ---------- model : str Name of orientation relationship. - target : Crystal + target : Crystal, optional Crystal to transform to. Providing this parameter allows specification of non-standard lattice parameters. - Defaults to standard parameters of target lattice. + Default is inferred from selected model and uses standard lattice parameters. Returns ------- diff --git a/python/damask/_orientation.py b/python/damask/_orientation.py index 72e289859..ca757166a 100644 --- a/python/damask/_orientation.py +++ b/python/damask/_orientation.py @@ -878,8 +878,10 @@ class Orientation(Rotation,Crystal): ---------- model : str Orientation relationship model selected from self.orientation_relationships. - target : Crystal + target : Crystal, optional Crystal to transform to. + Providing this parameter allows specification of non-standard lattice parameters. + Default is inferred from selected model and uses standard lattice parameters. Returns ------- From 7ea8f5a7dae67940ab574639b268c4f12d65c65b Mon Sep 17 00:00:00 2001 From: Test User Date: Mon, 6 Nov 2023 23:29:17 +0100 Subject: [PATCH 26/84] [skip ci] updated version information after successful test of v3.0.0-alpha7-908-g8ff6e28c9 --- VERSION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/VERSION b/VERSION index 8e999f891..618224384 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -3.0.0-alpha7-904-g2a00de5fb +3.0.0-alpha7-908-g8ff6e28c9 From f687e0a4339d9eea5603d71b542dbde42a75db18 Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Mon, 6 Nov 2023 23:42:11 +0100 Subject: [PATCH 27/84] helpful error message --- python/damask/_crystal.py | 9 ++++++--- python/tests/test_Crystal.py | 8 ++++++++ 2 files changed, 14 insertions(+), 3 deletions(-) diff --git a/python/damask/_crystal.py b/python/damask/_crystal.py index f5034a4a4..714427e24 100644 --- a/python/damask/_crystal.py +++ b/python/damask/_crystal.py @@ -1200,10 +1200,13 @@ class Crystal(): raise KeyError(f'unknown orientation relationship "{model}"') sep = '-->' - search = self.lattice+sep+('' if target is None else target.lattice) # type: ignore - m_l,o_l = [transform.split(sep) for transform in orientation_relationships[model].keys() # type: ignore - if transform.startswith(search)][0] + search = self.lattice+sep+('' if target is None else target.lattice) # type: ignore + transform = [t for t in orientation_relationships[model].keys() if t.startswith(search)] # type: ignore + if len(transform) != 1: + raise ValueError(f'invalid target lattice "{search.split(sep)[1]}"') + + m_l,o_l = transform[0].split(sep) # type: ignore m_p,o_p = orientation_relationships[model][m_l+sep+o_l] other = Crystal(lattice=o_l) if target is None else target m_p = np.stack((self.to_frame(uvw=m_p[:,0] if len(m_p[0,0])==3 else util.Bravais_to_Miller(uvtw=m_p[:,0])), diff --git a/python/tests/test_Crystal.py b/python/tests/test_Crystal.py index bd4a7235b..8037e3a3b 100644 --- a/python/tests/test_Crystal.py +++ b/python/tests/test_Crystal.py @@ -110,3 +110,11 @@ class TestCrystal: for r in crystal.orientation_relationships: crystal.relation_operations(r) + @pytest.mark.parametrize('crystal', [Crystal(lattice='cF'), + Crystal(lattice='cI'), + Crystal(lattice='hP')]) + def test_related_invalid_target(self,crystal): + relationship = np.random.choice(crystal.orientation_relationships) + with pytest.raises(ValueError): + crystal.relation_operations(relationship,crystal) + From 1855bafd6a4a8ebb8963edc0a6c38d37aa1c3cc4 Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Tue, 7 Nov 2023 00:10:13 +0100 Subject: [PATCH 28/84] additional, self-explanatory tests --- .../tests/resources/Orientation/Bain-001.png | Bin 0 -> 16237 bytes .../tests/resources/Orientation/Bain-011.png | Bin 0 -> 17032 bytes .../tests/resources/Orientation/Bain-111.png | Bin 0 -> 16442 bytes python/tests/resources/Orientation/GT-001.png | Bin 0 -> 18737 bytes python/tests/resources/Orientation/GT-011.png | Bin 0 -> 22128 bytes python/tests/resources/Orientation/GT-111.png | Bin 0 -> 19784 bytes .../resources/Orientation/GT_prime-001.png | Bin 0 -> 19064 bytes .../resources/Orientation/GT_prime-011.png | Bin 0 -> 22377 bytes .../resources/Orientation/GT_prime-111.png | Bin 0 -> 19748 bytes python/tests/resources/Orientation/KS-001.png | Bin 0 -> 19682 bytes python/tests/resources/Orientation/KS-011.png | Bin 0 -> 22545 bytes python/tests/resources/Orientation/KS-111.png | Bin 0 -> 19933 bytes python/tests/resources/Orientation/NW-001.png | Bin 0 -> 17550 bytes python/tests/resources/Orientation/NW-011.png | Bin 0 -> 19698 bytes python/tests/resources/Orientation/NW-111.png | Bin 0 -> 18172 bytes .../resources/Orientation/Pitsch-001.png | Bin 0 -> 17799 bytes .../resources/Orientation/Pitsch-011.png | Bin 0 -> 19992 bytes .../resources/Orientation/Pitsch-111.png | Bin 0 -> 17860 bytes ...ensionX_2phase_irregularGrid.material.hdf5 | Bin 0 -> 1275490 bytes ...irregularGrid_tensionX_increment_0.dream3d | Bin 0 -> 30608 bytes ...rregularGrid_tensionX_increment_40.dream3d | Bin 0 -> 30608 bytes python/tests/test_Orientation.py | 24 ++++++++++++++++++ 22 files changed, 24 insertions(+) create mode 100644 python/tests/resources/Orientation/Bain-001.png create mode 100644 python/tests/resources/Orientation/Bain-011.png create mode 100644 python/tests/resources/Orientation/Bain-111.png create mode 100644 python/tests/resources/Orientation/GT-001.png create mode 100644 python/tests/resources/Orientation/GT-011.png create mode 100644 python/tests/resources/Orientation/GT-111.png create mode 100644 python/tests/resources/Orientation/GT_prime-001.png create mode 100644 python/tests/resources/Orientation/GT_prime-011.png create mode 100644 python/tests/resources/Orientation/GT_prime-111.png create mode 100644 python/tests/resources/Orientation/KS-001.png create mode 100644 python/tests/resources/Orientation/KS-011.png create mode 100644 python/tests/resources/Orientation/KS-111.png create mode 100644 python/tests/resources/Orientation/NW-001.png create mode 100644 python/tests/resources/Orientation/NW-011.png create mode 100644 python/tests/resources/Orientation/NW-111.png create mode 100644 python/tests/resources/Orientation/Pitsch-001.png create mode 100644 python/tests/resources/Orientation/Pitsch-011.png create mode 100644 python/tests/resources/Orientation/Pitsch-111.png create mode 100644 python/tests/resources/Result/2phase_irregularGrid_tensionX_2phase_irregularGrid.material.hdf5 create mode 100644 python/tests/resources/Result/2phase_irregularGrid_tensionX_increment_0.dream3d create mode 100644 python/tests/resources/Result/2phase_irregularGrid_tensionX_increment_40.dream3d diff --git a/python/tests/resources/Orientation/Bain-001.png b/python/tests/resources/Orientation/Bain-001.png new file mode 100644 index 0000000000000000000000000000000000000000..c46dc3b32735b667d5349b748a47064e606ba567 GIT binary 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+from PIL import Image from damask import Rotation from damask import Orientation @@ -520,3 +522,25 @@ class TestOrientation: def test_mul_invalid(self): with pytest.raises(TypeError): Orientation.from_random(lattice='cF')*np.ones(3) + + @pytest.mark.parametrize('OR',['KS','NW','GT','GT_prime','Bain','Pitsch']) + @pytest.mark.parametrize('pole',[[0,0,1],[0,1,1],[1,1,1]]) + def test_OR_plot(self,update,res_path,tmp_path,OR,pole): + # https://doi.org/10.3390/cryst13040663 for comparison + O = Orientation(lattice='cF') + poles = O.related(OR).to_pole(uvw=pole,with_symmetry=True).reshape(-1,3) + points = util.project_equal_area(poles,'z') + + fig, ax = plt.subplots() + c = plt.Circle((0,0),1, color='k',fill=False) + ax.add_patch(c) + ax.scatter(points[:,0],points[:,1]) + ax.set_aspect('equal', 'box') + fname=f'{OR}-{"".join(map(str,pole))}.png' + plt.axis('off') + plt.savefig(tmp_path/fname) + if update: plt.savefig(res_path/fname) + current = np.array(Image.open(tmp_path/fname)) + reference = np.array(Image.open(res_path/fname)) + assert np.allclose(current,reference) + From b332fd2b30e0d7d9730d63f47a22ad9caac90941 Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Tue, 7 Nov 2023 11:32:40 +0100 Subject: [PATCH 29/84] restored type hints for some strange reason, ignore is needed for damask.Crystal.__eq__ --- python/damask/_crystal.py | 4 ++-- python/damask/_orientation.py | 2 +- python/damask/_table.py | 5 +++++ 3 files changed, 8 insertions(+), 3 deletions(-) diff --git a/python/damask/_crystal.py b/python/damask/_crystal.py index 714427e24..e754a65f8 100644 --- a/python/damask/_crystal.py +++ b/python/damask/_crystal.py @@ -547,7 +547,7 @@ class Crystal(): def __eq__(self, - other): + other: object) -> bool: """ Return self==other. @@ -562,7 +562,7 @@ class Crystal(): return (NotImplemented if not isinstance(other, Crystal) else self.lattice == other.lattice and self.parameters == other.parameters and - self.family == other.family) + self.family == other.family) # type: ignore @property def parameters(self) -> Optional[Tuple]: diff --git a/python/damask/_orientation.py b/python/damask/_orientation.py index ca757166a..b28ba2aae 100644 --- a/python/damask/_orientation.py +++ b/python/damask/_orientation.py @@ -122,7 +122,7 @@ class Orientation(Rotation,Crystal): def __eq__(self, - other: Union[object,MyType]) -> bool: + other: object) -> bool: """ Return self==other. diff --git a/python/damask/_table.py b/python/damask/_table.py index ac19f761a..9608f0d13 100644 --- a/python/damask/_table.py +++ b/python/damask/_table.py @@ -57,6 +57,11 @@ class Table: Test equality of other. + Parameters + ---------- + other : Table + Table to check for equality. + """ return NotImplemented if not isinstance(other,Table) else \ self.shapes == other.shapes and self.data.equals(other.data) From f8f8e3b4415d3f8d5f3528dd406a937536de61ab Mon Sep 17 00:00:00 2001 From: Test User Date: Tue, 7 Nov 2023 21:23:41 +0100 Subject: [PATCH 30/84] [skip ci] updated version information after successful test of v3.0.0-alpha7-919-g5877eafa6 --- VERSION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/VERSION b/VERSION index 618224384..cf2c412b8 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -3.0.0-alpha7-908-g8ff6e28c9 +3.0.0-alpha7-919-g5877eafa6 From 2f66a868308c384fd7a469538a07b533dbf7474d Mon Sep 17 00:00:00 2001 From: Test User Date: Tue, 14 Nov 2023 14:52:26 +0100 Subject: [PATCH 31/84] [skip ci] updated version information after successful test of v3.0.0-alpha7-924-g66b36d471 --- VERSION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/VERSION b/VERSION index cf2c412b8..ab43e3eff 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -3.0.0-alpha7-919-g5877eafa6 +3.0.0-alpha7-924-g66b36d471 From b642c9cf00e1024901fcce92f203ddd9d054f913 Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Tue, 14 Nov 2023 16:54:25 +0100 Subject: [PATCH 32/84] v3.0.0-alpha8 --- VERSION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/VERSION b/VERSION index ab43e3eff..0e304accd 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -3.0.0-alpha7-924-g66b36d471 +v3.0.0-alpha8 From 9a12bf5ee7ac83cbd2b25a538d5b8aa476bcabbd Mon Sep 17 00:00:00 2001 From: Test User Date: Tue, 14 Nov 2023 21:15:45 +0100 Subject: [PATCH 33/84] [skip ci] updated version information after successful test of v3.0.0-alpha8 --- VERSION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/VERSION b/VERSION index 0e304accd..cd2bca7c2 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -v3.0.0-alpha8 +3.0.0-alpha8 From d8baf3c754f46a65f44fe7cf61a4a32bebaaea0b Mon Sep 17 00:00:00 2001 From: Philip Eisenlohr Date: Sat, 18 Nov 2023 20:00:55 -0500 Subject: [PATCH 34/84] reset field to average of bounds avoiding casting masked NaNs --- python/damask/_colormap.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/python/damask/_colormap.py b/python/damask/_colormap.py index ccb8d3d70..5af72acd8 100644 --- a/python/damask/_colormap.py +++ b/python/damask/_colormap.py @@ -296,7 +296,7 @@ class Colormap(mpl.colors.ListedColormap): RGBA image of shaded data. """ - mask = np.logical_not(np.isnan(field) if gap is None else \ + mask = np.logical_not(np.isnan(field) if gap is None else np.logical_or (np.isnan(field), field == gap)) # mask NaN (and gap if present) l,r = (field[mask].min(),field[mask].max()) if bounds is None else \ @@ -305,9 +305,11 @@ class Colormap(mpl.colors.ListedColormap): if abs(delta := r-l) * 1e8 <= (avg := 0.5*abs(r+l)): # delta is similar to numerical noise l,r = (l-0.5*avg*np.sign(delta),r+0.5*avg*np.sign(delta)) # extend range to have actual data centered within + field[np.isnan(field)] = (l+r)/2 + return Image.fromarray( (np.dstack(( - self.colors[(np.round(np.clip((field-l)/delta,0.0,1.0)*(self.N-1))).astype(np.uint16),:3], + self.colors[np.round(np.clip((field-l)/(r-l),0.0,1.0)*(self.N-1)).astype(np.uint16),:3], mask.astype(float) ) )*255 From 57fab5db1cf2fe0958322aa4c7dd356e408afb51 Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Mon, 20 Nov 2023 09:39:59 +0100 Subject: [PATCH 35/84] restart (currently) not supported for mesh solver --- src/CLI.f90 | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/src/CLI.f90 b/src/CLI.f90 index 968186f3d..323019815 100644 --- a/src/CLI.f90 +++ b/src/CLI.f90 @@ -126,7 +126,9 @@ subroutine CLI_init() print'(1x,a)', ' --numerics (-n, --numericsconfig)' print'(1x,a)', ' --jobname (-j, --job)' print'(1x,a)', ' --workingdir (-w, --wd, --workingdirectory)' +#if defined(GRID) print'(1x,a)', ' --restart (-r, --rs)' +#endif print'(1x,a)', ' --help (-h)' print'(/,1x,a)','-----------------------------------------------------------------------' print'(1x,a)', 'Mandatory arguments:' @@ -146,12 +148,14 @@ subroutine CLI_init() print'(/,1x,a)',' --workingdirectory WORKINGDIRECTORY' print'(1x,a)', ' specify the base directory of relative paths.' print'(1x,a)', ' Defaults to the current working directory' +#if defined(GRID) print'(/,1x,a)',' --restart N' print'(1x,a)', ' read in increment N and continues with calculating' print'(1x,a)', ' increment N+1, N+2, ... based on this' print'(1x,a)', ' works only if the restart information for increment N' print'(1x,a)', ' is available in JOBNAME_restart.hdf5' print'(1x,a)', ' append to existing results file JOBNAME.hdf5' +#endif print'(/,1x,a)','-----------------------------------------------------------------------' print'(1x,a)', 'Help:' print'(/,1x,a)',' --help' @@ -175,11 +179,13 @@ subroutine CLI_init() case ('-w', '--wd', '--workingdir', '--workingdirectory') if (.not. hasArg) call IO_error(610,ext_msg='--workingdirectory') workingDirArg = getArg(i+1) +#if defined(GRID) case ('-r', '--rs', '--restart') if (.not. hasArg) call IO_error(610,ext_msg='--jobname') arg = getArg(i+1) CLI_restartInc = IO_strAsInt(arg) if (CLI_restartInc < 0 .or. stat /= 0) call IO_error(611,ext_msg=arg) +#endif end select end do From 39dacbc9727d154e367ce8aab3e1344b6d377794 Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Mon, 20 Nov 2023 11:49:59 +0100 Subject: [PATCH 36/84] easier to understand --- python/damask/_colormap.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/damask/_colormap.py b/python/damask/_colormap.py index 5af72acd8..ad7aa286b 100644 --- a/python/damask/_colormap.py +++ b/python/damask/_colormap.py @@ -297,7 +297,7 @@ class Colormap(mpl.colors.ListedColormap): """ mask = np.logical_not(np.isnan(field) if gap is None else - np.logical_or (np.isnan(field), field == gap)) # mask NaN (and gap if present) + np.logical_or(np.isnan(field), field == gap)) # mask NaN (and gap if present) l,r = (field[mask].min(),field[mask].max()) if bounds is None else \ (bounds[0],bounds[1]) From c0a97a93fe4a2606a45eff926f412ceb3137762e Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Mon, 20 Nov 2023 12:27:23 +0100 Subject: [PATCH 37/84] do not overwrite input, use specialized function --- python/damask/_colormap.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/python/damask/_colormap.py b/python/damask/_colormap.py index ad7aa286b..f4834da78 100644 --- a/python/damask/_colormap.py +++ b/python/damask/_colormap.py @@ -305,11 +305,11 @@ class Colormap(mpl.colors.ListedColormap): if abs(delta := r-l) * 1e8 <= (avg := 0.5*abs(r+l)): # delta is similar to numerical noise l,r = (l-0.5*avg*np.sign(delta),r+0.5*avg*np.sign(delta)) # extend range to have actual data centered within - field[np.isnan(field)] = (l+r)/2 + field_ = np.nan_to_num(field, nan=(l+r)/2, posinf=r, neginf=l) return Image.fromarray( (np.dstack(( - self.colors[np.round(np.clip((field-l)/(r-l),0.0,1.0)*(self.N-1)).astype(np.uint16),:3], + self.colors[np.round(np.clip((field_-l)/(r-l),0.0,1.0)*(self.N-1)).astype(np.uint16),:3], mask.astype(float) ) )*255 From fbdab24f4b9285cf98e114138e507e188b617081 Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Mon, 20 Nov 2023 13:27:34 +0100 Subject: [PATCH 38/84] bugfix: used wrong definition looks like a copy and paste error --- src/crystal.f90 | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/crystal.f90 b/src/crystal.f90 index 32040bce4..66930d488 100644 --- a/src/crystal.f90 +++ b/src/crystal.f90 @@ -512,13 +512,13 @@ function crystal_C66_twin(Ntwin,C66,lattice,CoverA) select case(lattice) case('cF') - coordinateSystem = buildCoordinateSystem(Ntwin,CF_NSLIPSYSTEM,CF_SYSTEMTWIN,& + coordinateSystem = buildCoordinateSystem(Ntwin,CF_NTWINSYSTEM,CF_SYSTEMTWIN,& lattice,0.0_pREAL) case('cI') - coordinateSystem = buildCoordinateSystem(Ntwin,CI_NSLIPSYSTEM,CI_SYSTEMTWIN,& + coordinateSystem = buildCoordinateSystem(Ntwin,CI_NTWINSYSTEM,CI_SYSTEMTWIN,& lattice,0.0_pREAL) case('hP') - coordinateSystem = buildCoordinateSystem(Ntwin,HP_NSLIPSYSTEM,HP_SYSTEMTWIN,& + coordinateSystem = buildCoordinateSystem(Ntwin,HP_NTWINSYSTEM,HP_SYSTEMTWIN,& lattice,cOverA) case default call IO_error(137,ext_msg='crystal_C66_twin: '//trim(lattice)) From 5db4c3ecf6bd68d574c3bee32d085613fb6e25ff Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Mon, 20 Nov 2023 16:19:20 +0100 Subject: [PATCH 39/84] document --- src/crystal.f90 | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/crystal.f90 b/src/crystal.f90 index 66930d488..62679e309 100644 --- a/src/crystal.f90 +++ b/src/crystal.f90 @@ -525,7 +525,7 @@ function crystal_C66_twin(Ntwin,C66,lattice,CoverA) end select do i = 1, sum(Ntwin) - call R%fromAxisAngle([coordinateSystem(1:3,2,i),PI],P=1) ! ToDo: Why always 180 deg? + call R%fromAxisAngle([coordinateSystem(1:3,2,i),PI]) ! mirror on habit (twin shear) plane crystal_C66_twin(1:6,1:6,i) = R%rotStiffness(C66) end do From 5d6fcd497b716a35ad2d7095e867d47fedd93535 Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Mon, 20 Nov 2023 16:47:39 +0100 Subject: [PATCH 40/84] icc does not exist anymore --- .github/workflows/Fortran.yml | 34 +++------------------------------- 1 file changed, 3 insertions(+), 31 deletions(-) diff --git a/.github/workflows/Fortran.yml b/.github/workflows/Fortran.yml index c6826bb83..ae28e1611 100644 --- a/.github/workflows/Fortran.yml +++ b/.github/workflows/Fortran.yml @@ -91,13 +91,8 @@ jobs: runs-on: [ubuntu-22.04] strategy: - matrix: - intel_v: [classic, llvm] # Variant of Intel compilers fail-fast: false - env: - INTEL_V: ${{ matrix.intel_v }} - steps: - uses: actions/checkout@v3 @@ -133,7 +128,7 @@ jobs: sed -i "1719s/if not os.path.isfile(os.path.join(self.packageDir,'configure')):/if True:/g" \ ./petsc-${PETSC_VERSION}/config/BuildSystem/config/package.py export PETSC_DIR=${PWD}/petsc-${PETSC_VERSION} - export PETSC_ARCH=intel-${INTEL_V} + export PETSC_ARCH=intel printenv >> $GITHUB_ENV - name: PETSc - Cache installation @@ -141,21 +136,9 @@ jobs: uses: actions/cache@v3 with: path: petsc-${{ env.PETSC_VERSION }} - key: petsc-${{ env.PETSC_VERSION }}-intel-${{ matrix.intel_v }}-${{ hashFiles('**/petscversion.h') }} + key: petsc-${{ env.PETSC_VERSION }}-intel-${{ hashFiles('**/petscversion.h') }} - - name: PETSc - Install (classic) - if: contains( matrix.intel_v, 'classic') - run: | - cd petsc-${PETSC_VERSION} - ./configure \ - --with-fc='mpiifort -fc=ifort -diag-disable=10441' \ - --with-cc='mpiicc -cc=icc -diag-disable=10441' \ - --with-cxx='mpiicpc -cxx=icpc -diag-disable=10441' \ - --download-fftw --download-hdf5 --download-hdf5-fortran-bindings=1 --download-zlib - make all - - - name: PETSc - Install (LLVM) - if: contains( matrix.intel_v, 'llvm') + - name: PETSc - Install run: | cd petsc-${PETSC_VERSION} ./configure \ @@ -165,20 +148,9 @@ jobs: --download-fftw --download-hdf5 --download-hdf5-fortran-bindings=1 --download-zlib make all - - name: DAMASK - Compile - if: contains( matrix.intel_v, 'classic') - run: | - cmake -B build/grid -DDAMASK_SOLVER=grid -DCMAKE_INSTALL_PREFIX=${PWD} - cmake --build build/grid --parallel - cmake --install build/grid - cmake -B build/mesh -DDAMASK_SOLVER=mesh -DCMAKE_INSTALL_PREFIX=${PWD} - cmake --build build/mesh --parallel - cmake --install build/mesh - # ifx has issue with openMP # https://community.intel.com/t5/Intel-Fortran-Compiler/ifx-ICE-and-SEGFAULT/m-p/1459877 - name: DAMASK - Compile - if: contains( matrix.intel_v, 'llvm') run: | cmake -B build/grid -DDAMASK_SOLVER=grid -DCMAKE_INSTALL_PREFIX=${PWD} -DOPENMP=OFF cmake --build build/grid --parallel From b77a4145d0b96560e88e11e4e1df216f16b4abf9 Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Mon, 20 Nov 2023 19:31:56 +0100 Subject: [PATCH 41/84] no need to expand, is a property of the family --- src/crystal.f90 | 33 ++++++--------------------------- 1 file changed, 6 insertions(+), 27 deletions(-) diff --git a/src/crystal.f90 b/src/crystal.f90 index 62679e309..8e820b515 100644 --- a/src/crystal.f90 +++ b/src/crystal.f90 @@ -432,37 +432,17 @@ function crystal_characteristicShear_Twin(Ntwin,lattice,CoverA) result(character integer :: & a, & !< index of active system - p, & !< index in potential system list f, & !< index of my family s !< index of my system in current family - integer, dimension(HP_NTWIN), parameter :: & - HP_SHEARTWIN = reshape( [& + integer, dimension(size(HP_NTWINSYSTEM)), parameter :: & + HP_SHEARTWIN = [& 1, & ! <-10.1>{10.2} - 1, & - 1, & - 1, & - 1, & - 1, & 2, & ! <11.6>{-1-1.1} - 2, & - 2, & - 2, & - 2, & - 2, & 3, & ! <10.-2>{10.1} - 3, & - 3, & - 3, & - 3, & - 3, & - 4, & ! <11.-3>{11.2} - 4, & - 4, & - 4, & - 4, & - 4 & - ],[HP_NTWIN]) !< indicator to formulas below + 4 & ! <11.-3>{11.2} + ] !< indicator to formulas below + a = 0 myFamilies: do f = 1,size(Ntwin,1) @@ -474,8 +454,7 @@ function crystal_characteristicShear_Twin(Ntwin,lattice,CoverA) result(character case('hP') if (cOverA < 1.0_pREAL .or. cOverA > 2.0_pREAL) & call IO_error(131,ext_msg='crystal_characteristicShear_Twin') - p = sum(HP_NTWINSYSTEM(1:f-1))+s - select case(HP_SHEARTWIN(p)) ! from Christian & Mahajan 1995 p.29 + select case(HP_SHEARTWIN(f)) ! from Christian & Mahajan 1995 p.29 case (1) ! <-10.1>{10.2} characteristicShear(a) = (3.0_pREAL-cOverA**2)/sqrt(3.0_pREAL)/CoverA case (2) ! <11.6>{-1-1.1} From eddeb8b695e778457343e4f7a34e136a87e84dc9 Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Mon, 20 Nov 2023 23:58:34 +0100 Subject: [PATCH 42/84] use container with oneapi installation avoids long installation --- .github/workflows/Fortran.yml | 18 +++++++----------- 1 file changed, 7 insertions(+), 11 deletions(-) diff --git a/.github/workflows/Fortran.yml b/.github/workflows/Fortran.yml index ae28e1611..f329e4d53 100644 --- a/.github/workflows/Fortran.yml +++ b/.github/workflows/Fortran.yml @@ -88,7 +88,9 @@ jobs: intel: - runs-on: [ubuntu-22.04] + runs-on: ubuntu-22.04 + container: + image: intel/oneapi-hpckit:latest strategy: fail-fast: false @@ -98,16 +100,10 @@ jobs: - name: Intel - Install run: | - wget -q https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS-2023.PUB - sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS-2023.PUB - rm GPG-PUB-KEY-INTEL-SW-PRODUCTS-2023.PUB - echo "deb https://apt.repos.intel.com/oneapi all main" | sudo tee /etc/apt/sources.list.d/oneAPI.list - sudo apt-get update - sudo apt-get install \ - intel-basekit \ - intel-oneapi-compiler-dpcpp-cpp-and-cpp-classic intel-oneapi-compiler-fortran \ - intel-oneapi-openmp intel-oneapi-mkl-devel - source /opt/intel/oneapi/setvars.sh + apt-get update --allow-insecure-repositories + apt-get install --allow-unauthenticated -y \ + cmake build-essential autoconf libtool \ + zlib1g-dev printenv >> $GITHUB_ENV - name: PETSc - Cache download From 71a7d8e3d6b741259baad5843a81ced412e2b185 Mon Sep 17 00:00:00 2001 From: Test User Date: Tue, 21 Nov 2023 00:03:21 +0100 Subject: [PATCH 43/84] [skip ci] updated version information after successful test of v3.0.0-alpha8-10-gb63dd758d --- VERSION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/VERSION b/VERSION index cd2bca7c2..f57289c7c 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -3.0.0-alpha8 +3.0.0-alpha8-10-gb63dd758d From 693f3cf07ed13f657094567a27107030ec301a0e Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Tue, 21 Nov 2023 00:05:40 +0100 Subject: [PATCH 44/84] include most recent Python version --- .github/workflows/Python.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/Python.yml b/.github/workflows/Python.yml index 82f7dbb2e..1e0a3c1ce 100644 --- a/.github/workflows/Python.yml +++ b/.github/workflows/Python.yml @@ -9,7 +9,7 @@ jobs: strategy: matrix: - python-version: ['3.8', '3.9', '3.10', '3.11'] + python-version: ['3.8', '3.9', '3.10', '3.11', '3.12'] os: [ubuntu-latest, macos-latest, windows-latest] fail-fast: false From d2b80c350e107dad85f41ad4e8fc40ecee6fcbe1 Mon Sep 17 00:00:00 2001 From: Test User Date: Tue, 21 Nov 2023 08:55:48 +0100 Subject: [PATCH 45/84] [skip ci] updated version information after successful test of v3.0.0-alpha8-13-gb85a754ae --- VERSION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/VERSION b/VERSION index f57289c7c..22b8095d9 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -3.0.0-alpha8-10-gb63dd758d +3.0.0-alpha8-13-gb85a754ae From e3e6aa2fc826c515fbc78756364d9e688bf333f4 Mon Sep 17 00:00:00 2001 From: Test User Date: Tue, 21 Nov 2023 17:18:39 +0100 Subject: [PATCH 46/84] [skip ci] updated version information after successful test of v3.0.0-alpha8-16-g1be424b69 --- VERSION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/VERSION b/VERSION index 22b8095d9..ad941bfdb 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -3.0.0-alpha8-13-gb85a754ae +3.0.0-alpha8-16-g1be424b69 From 19005faa9d44d1eddc120b323191acccaf77a4fa Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Wed, 22 Nov 2023 08:01:30 +0100 Subject: [PATCH 47/84] include most recent GNU compiler --- .github/workflows/Fortran.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/Fortran.yml b/.github/workflows/Fortran.yml index f329e4d53..a8bdac5fc 100644 --- a/.github/workflows/Fortran.yml +++ b/.github/workflows/Fortran.yml @@ -17,7 +17,7 @@ jobs: strategy: matrix: - gcc_v: [9, 10, 11, 12] + gcc_v: [9, 10, 11, 12, 13] fail-fast: false env: From 40c0fbf9aa177e618a4e81759fb04317a83e477c Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Wed, 22 Nov 2023 08:02:31 +0100 Subject: [PATCH 48/84] was fixed --- .github/workflows/Fortran.yml | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/.github/workflows/Fortran.yml b/.github/workflows/Fortran.yml index a8bdac5fc..1a817b7ce 100644 --- a/.github/workflows/Fortran.yml +++ b/.github/workflows/Fortran.yml @@ -144,14 +144,12 @@ jobs: --download-fftw --download-hdf5 --download-hdf5-fortran-bindings=1 --download-zlib make all - # ifx has issue with openMP - # https://community.intel.com/t5/Intel-Fortran-Compiler/ifx-ICE-and-SEGFAULT/m-p/1459877 - name: DAMASK - Compile run: | - cmake -B build/grid -DDAMASK_SOLVER=grid -DCMAKE_INSTALL_PREFIX=${PWD} -DOPENMP=OFF + cmake -B build/grid -DDAMASK_SOLVER=grid -DCMAKE_INSTALL_PREFIX=${PWD} cmake --build build/grid --parallel cmake --install build/grid - cmake -B build/mesh -DDAMASK_SOLVER=mesh -DCMAKE_INSTALL_PREFIX=${PWD} -DOPENMP=OFF + cmake -B build/mesh -DDAMASK_SOLVER=mesh -DCMAKE_INSTALL_PREFIX=${PWD} cmake --build build/mesh --parallel cmake --install build/mesh From 46a087e9ed24830c67c229665f16bda7c507d9b2 Mon Sep 17 00:00:00 2001 From: Test User Date: Wed, 22 Nov 2023 13:06:13 +0100 Subject: [PATCH 49/84] [skip ci] updated version information after successful test of v3.0.0-alpha8-20-g1aa698c46 --- VERSION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/VERSION b/VERSION index ad941bfdb..c196b1e31 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -3.0.0-alpha8-16-g1be424b69 +3.0.0-alpha8-20-g1aa698c46 From f6c463d2a63381942b710f46e5a259b384f1a4ce Mon Sep 17 00:00:00 2001 From: Test User Date: Wed, 22 Nov 2023 18:40:02 +0100 Subject: [PATCH 50/84] [skip ci] updated version information after successful test of v3.0.0-alpha8-25-g0a73cd886 --- VERSION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/VERSION b/VERSION index c196b1e31..01b4c02b9 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -3.0.0-alpha8-20-g1aa698c46 +3.0.0-alpha8-25-g0a73cd886 From 34db73b06856853d0c976f19047e7ce7bcc2c3ce Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Wed, 22 Nov 2023 20:22:05 +0100 Subject: [PATCH 51/84] more logical order --- src/crystal.f90 | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/src/crystal.f90 b/src/crystal.f90 index 8e820b515..8f0b0bfb7 100644 --- a/src/crystal.f90 +++ b/src/crystal.f90 @@ -444,16 +444,16 @@ function crystal_characteristicShear_Twin(Ntwin,lattice,CoverA) result(character ] !< indicator to formulas below - a = 0 - myFamilies: do f = 1,size(Ntwin,1) - mySystems: do s = 1,Ntwin(f) - a = a + 1 - select case(lattice) - case('cF','cI') - characteristicShear(a) = 0.5_pREAL*sqrt(2.0_pREAL) - case('hP') - if (cOverA < 1.0_pREAL .or. cOverA > 2.0_pREAL) & - call IO_error(131,ext_msg='crystal_characteristicShear_Twin') + select case(lattice) + case('cF','cI') + characteristicShear = 0.5_pREAL*sqrt(2.0_pREAL) + case('hP') + if (cOverA < 1.0_pREAL .or. cOverA > 2.0_pREAL) & + call IO_error(131,ext_msg='crystal_characteristicShear_Twin') + a = 0 + myFamilies: do f = 1,size(Ntwin,1) + mySystems: do s = 1,Ntwin(f) + a = a + 1 select case(HP_SHEARTWIN(f)) ! from Christian & Mahajan 1995 p.29 case (1) ! <-10.1>{10.2} characteristicShear(a) = (3.0_pREAL-cOverA**2)/sqrt(3.0_pREAL)/CoverA @@ -464,11 +464,11 @@ function crystal_characteristicShear_Twin(Ntwin,lattice,CoverA) result(character case (4) ! <11.-3>{11.2} characteristicShear(a) = 2.0_pREAL*(cOverA**2-2.0_pREAL)/3.0_pREAL/cOverA end select - case default - call IO_error(137,ext_msg='crystal_characteristicShear_Twin: '//trim(lattice)) - end select - end do mySystems - end do myFamilies + end do mySystems + end do myFamilies + case default + call IO_error(137,ext_msg='crystal_characteristicShear_Twin: '//trim(lattice)) + end select end function crystal_characteristicShear_Twin From eaf65e56658a15c7ebcd5b707c6d722117a885e1 Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Wed, 22 Nov 2023 20:31:35 +0100 Subject: [PATCH 52/84] loop not needed --- src/crystal.f90 | 30 ++++++++++++++---------------- 1 file changed, 14 insertions(+), 16 deletions(-) diff --git a/src/crystal.f90 b/src/crystal.f90 index 8f0b0bfb7..6690955bf 100644 --- a/src/crystal.f90 +++ b/src/crystal.f90 @@ -431,9 +431,8 @@ function crystal_characteristicShear_Twin(Ntwin,lattice,CoverA) result(character real(pREAL), dimension(sum(Ntwin)) :: characteristicShear integer :: & - a, & !< index of active system f, & !< index of my family - s !< index of my system in current family + s, e integer, dimension(size(HP_NTWINSYSTEM)), parameter :: & HP_SHEARTWIN = [& @@ -450,21 +449,20 @@ function crystal_characteristicShear_Twin(Ntwin,lattice,CoverA) result(character case('hP') if (cOverA < 1.0_pREAL .or. cOverA > 2.0_pREAL) & call IO_error(131,ext_msg='crystal_characteristicShear_Twin') - a = 0 + myFamilies: do f = 1,size(Ntwin,1) - mySystems: do s = 1,Ntwin(f) - a = a + 1 - select case(HP_SHEARTWIN(f)) ! from Christian & Mahajan 1995 p.29 - case (1) ! <-10.1>{10.2} - characteristicShear(a) = (3.0_pREAL-cOverA**2)/sqrt(3.0_pREAL)/CoverA - case (2) ! <11.6>{-1-1.1} - characteristicShear(a) = 1.0_pREAL/cOverA - case (3) ! <10.-2>{10.1} - characteristicShear(a) = (4.0_pREAL*cOverA**2-9.0_pREAL)/sqrt(48.0_pREAL)/cOverA - case (4) ! <11.-3>{11.2} - characteristicShear(a) = 2.0_pREAL*(cOverA**2-2.0_pREAL)/3.0_pREAL/cOverA - end select - end do mySystems + s = sum(Ntwin(:f-1)) + 1 + e = sum(Ntwin(:f)) + select case(HP_SHEARTWIN(f)) ! from Christian & Mahajan 1995 p.29 + case (1) ! <-10.1>{10.2} + characteristicShear(s:e) = (3.0_pREAL-cOverA**2)/sqrt(3.0_pREAL)/CoverA + case (2) ! <11.6>{-1-1.1} + characteristicShear(s:e) = 1.0_pREAL/cOverA + case (3) ! <10.-2>{10.1} + characteristicShear(s:e) = (4.0_pREAL*cOverA**2-9.0_pREAL)/sqrt(48.0_pREAL)/cOverA + case (4) ! <11.-3>{11.2} + characteristicShear(s:e) = 2.0_pREAL*(cOverA**2-2.0_pREAL)/3.0_pREAL/cOverA + end select end do myFamilies case default call IO_error(137,ext_msg='crystal_characteristicShear_Twin: '//trim(lattice)) From f9c34e799eb4cbb79114c3699c9dd813141cf969 Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Wed, 22 Nov 2023 20:32:28 +0100 Subject: [PATCH 53/84] indicator not needed --- src/crystal.f90 | 18 +++++------------- 1 file changed, 5 insertions(+), 13 deletions(-) diff --git a/src/crystal.f90 b/src/crystal.f90 index 6690955bf..5aecb45e5 100644 --- a/src/crystal.f90 +++ b/src/crystal.f90 @@ -434,14 +434,6 @@ function crystal_characteristicShear_Twin(Ntwin,lattice,CoverA) result(character f, & !< index of my family s, e - integer, dimension(size(HP_NTWINSYSTEM)), parameter :: & - HP_SHEARTWIN = [& - 1, & ! <-10.1>{10.2} - 2, & ! <11.6>{-1-1.1} - 3, & ! <10.-2>{10.1} - 4 & ! <11.-3>{11.2} - ] !< indicator to formulas below - select case(lattice) case('cF','cI') @@ -453,14 +445,14 @@ function crystal_characteristicShear_Twin(Ntwin,lattice,CoverA) result(character myFamilies: do f = 1,size(Ntwin,1) s = sum(Ntwin(:f-1)) + 1 e = sum(Ntwin(:f)) - select case(HP_SHEARTWIN(f)) ! from Christian & Mahajan 1995 p.29 - case (1) ! <-10.1>{10.2} + select case(f) ! from Christian & Mahajan 1995 p.29 + case (1) ! <-10.1>{10.2} characteristicShear(s:e) = (3.0_pREAL-cOverA**2)/sqrt(3.0_pREAL)/CoverA - case (2) ! <11.6>{-1-1.1} + case (2) ! <11.6>{-1-1.1} characteristicShear(s:e) = 1.0_pREAL/cOverA - case (3) ! <10.-2>{10.1} + case (3) ! <10.-2>{10.1} characteristicShear(s:e) = (4.0_pREAL*cOverA**2-9.0_pREAL)/sqrt(48.0_pREAL)/cOverA - case (4) ! <11.-3>{11.2} + case (4) ! <11.-3>{11.2} characteristicShear(s:e) = 2.0_pREAL*(cOverA**2-2.0_pREAL)/3.0_pREAL/cOverA end select end do myFamilies From b9e41732d4cc60c553be077a7f36adfd70cd02eb Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Thu, 23 Nov 2023 11:29:18 +0100 Subject: [PATCH 54/84] documented --- src/crystal.f90 | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/crystal.f90 b/src/crystal.f90 index 5aecb45e5..f0dc1afcb 100644 --- a/src/crystal.f90 +++ b/src/crystal.f90 @@ -436,16 +436,16 @@ function crystal_characteristicShear_Twin(Ntwin,lattice,CoverA) result(character select case(lattice) - case('cF','cI') + case('cF','cI') ! 10.1016/0079-6425(94)00007-7, Table 1 characteristicShear = 0.5_pREAL*sqrt(2.0_pREAL) - case('hP') + case('hP') ! 10.1016/0079-6425(94)00007-7, Table 3 if (cOverA < 1.0_pREAL .or. cOverA > 2.0_pREAL) & call IO_error(131,ext_msg='crystal_characteristicShear_Twin') myFamilies: do f = 1,size(Ntwin,1) s = sum(Ntwin(:f-1)) + 1 e = sum(Ntwin(:f)) - select case(f) ! from Christian & Mahajan 1995 p.29 + select case(f) case (1) ! <-10.1>{10.2} characteristicShear(s:e) = (3.0_pREAL-cOverA**2)/sqrt(3.0_pREAL)/CoverA case (2) ! <11.6>{-1-1.1} From 0ec8317202ad9bfe06697ee83f6f98fe70292dbd Mon Sep 17 00:00:00 2001 From: Test User Date: Fri, 24 Nov 2023 12:14:27 +0100 Subject: [PATCH 55/84] [skip ci] updated version information after successful test of v3.0.0-alpha8-31-gbfa1cf677 --- VERSION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/VERSION b/VERSION index 01b4c02b9..798250864 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -3.0.0-alpha8-25-g0a73cd886 +3.0.0-alpha8-31-gbfa1cf677 From c21afafce5e5f3e9956c7da4263f20764ab31c38 Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Sat, 25 Nov 2023 10:27:08 +0100 Subject: [PATCH 56/84] PETSc 3.20.1 --- .github/workflows/Fortran.yml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/Fortran.yml b/.github/workflows/Fortran.yml index 1a817b7ce..24fd3bdc6 100644 --- a/.github/workflows/Fortran.yml +++ b/.github/workflows/Fortran.yml @@ -2,7 +2,7 @@ name: Grid and Mesh Solver on: [push] env: - PETSC_VERSION: '3.18.4' + PETSC_VERSION: '3.20.1' HOMEBREW_NO_ANALYTICS: 'ON' # Make Homebrew installation a little quicker HOMEBREW_NO_AUTO_UPDATE: 'ON' HOMEBREW_NO_BOTTLE_SOURCE_FALLBACK: 'ON' @@ -47,7 +47,7 @@ jobs: - name: PETSc - Download if: steps.petsc-download.outputs.cache-hit != 'true' run: | - wget -q https://ftp.mcs.anl.gov/pub/petsc/release-snapshots/petsc-${PETSC_VERSION}.tar.gz -P download + wget -q https://web.cels.anl.gov/projects/petsc/download/release-snapshots/petsc-${PETSC_VERSION}.tar.gz -P download - name: PETSc - Prepare run: | @@ -116,12 +116,12 @@ jobs: - name: PETSc - Download if: steps.petsc-download.outputs.cache-hit != 'true' run: | - wget -q https://ftp.mcs.anl.gov/pub/petsc/release-snapshots/petsc-${PETSC_VERSION}.tar.gz -P download + wget -q https://web.cels.anl.gov/projects/petsc/download/release-snapshots/petsc-${PETSC_VERSION}.tar.gz -P download - name: PETSc - Prepare run: | tar -xf download/petsc-${PETSC_VERSION}.tar.gz -C . - sed -i "1719s/if not os.path.isfile(os.path.join(self.packageDir,'configure')):/if True:/g" \ + sed -i "1805s/if not os.path.isfile(os.path.join(self.packageDir,self.configureName)):/if True:/g" \ ./petsc-${PETSC_VERSION}/config/BuildSystem/config/package.py export PETSC_DIR=${PWD}/petsc-${PETSC_VERSION} export PETSC_ARCH=intel From 665e2d5b381db2e35aba3b21f8afe1f7eb2479ad Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Sat, 25 Nov 2023 15:03:46 +0100 Subject: [PATCH 57/84] test IO_read and IO_readlines --- src/test/test_IO.f90 | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/src/test/test_IO.f90 b/src/test/test_IO.f90 index cae7f76c4..a5a0ac545 100644 --- a/src/test/test_IO.f90 +++ b/src/test/test_IO.f90 @@ -1,4 +1,5 @@ module test_IO + use prec use IO implicit none(type,external) @@ -10,8 +11,29 @@ module test_IO subroutine test_IO_run() + real, dimension(30) :: rnd_real + character(len=size(rnd_real)) :: rnd_str + character(len=pSTRLEN), dimension(1) :: strarray_out + character(len=:), allocatable :: str_out + integer :: u,i + + call IO_selfTest() + call random_number(rnd_real) + + do i = 1, size(rnd_real) + rnd_str(i:i) = char(32 + int(rnd_real(i)*(127.-32.))) + end do + open(newunit=u,file='results.out',status='replace',form='formatted') + write(u,'(a)') rnd_str + close(u) + + str_out = IO_read('results.out') + if (rnd_str//IO_EOL /= str_out) error stop 'IO_read' + strarray_out = IO_readlines('results.out') + if (rnd_str /= strarray_out(1)) error stop 'IO_readlines' + end subroutine test_IO_run end module test_IO From 4fa292ad20dce785eddf79fbdba4fad0e1370db1 Mon Sep 17 00:00:00 2001 From: Test User Date: Sat, 25 Nov 2023 15:44:30 +0100 Subject: [PATCH 58/84] [skip ci] updated version information after successful test of v3.0.0-alpha8-34-g24864f2f2 --- VERSION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/VERSION b/VERSION index 798250864..70ad5033e 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -3.0.0-alpha8-31-gbfa1cf677 +3.0.0-alpha8-34-g24864f2f2 From febbddd36ab78168931af41d352a163093e4af8e Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Sat, 25 Nov 2023 15:59:00 +0100 Subject: [PATCH 59/84] starting to test system_routines --- src/materialpoint.f90 | 4 ++- src/system_routines.f90 | 42 +++++++++++++++++++++++++++++++ src/test/DAMASK_test.f90 | 7 +++++- src/test/test_system_routines.f90 | 17 +++++++++++++ 4 files changed, 68 insertions(+), 2 deletions(-) create mode 100644 src/test/test_system_routines.f90 diff --git a/src/materialpoint.f90 b/src/materialpoint.f90 index b92559a72..808cec146 100644 --- a/src/materialpoint.f90 +++ b/src/materialpoint.f90 @@ -5,8 +5,9 @@ !-------------------------------------------------------------------------------------------------- module materialpoint use parallelization - use signal use CLI + use system_routines + use signal use prec use misc use IO @@ -46,6 +47,7 @@ subroutine materialpoint_initAll() call parallelization_init() call CLI_init() ! grid and mesh commandline interface + call system_routines_init() call signal_init() call prec_init() call misc_init() diff --git a/src/system_routines.f90 b/src/system_routines.f90 index 5207b5b94..aa4a140d6 100644 --- a/src/system_routines.f90 +++ b/src/system_routines.f90 @@ -6,11 +6,14 @@ module system_routines use, intrinsic :: ISO_C_Binding use prec + use IO implicit none(type,external) private public :: & + system_routines_init, & + system_routines_selfTest, & setCWD, & getCWD, & getHostName, & @@ -93,6 +96,18 @@ module system_routines contains +!-------------------------------------------------------------------------------------------------- +!> @brief Do self test. +!-------------------------------------------------------------------------------------------------- +subroutine system_routines_init() + + print'(/,1x,a)', '<<<+- system_routines init -+>>>'; flush(IO_STDOUT) + + call system_routines_selfTest() + +end subroutine system_routines_init + + !-------------------------------------------------------------------------------------------------- !> @brief Set the current working directory. !-------------------------------------------------------------------------------------------------- @@ -103,6 +118,8 @@ logical function setCWD(path) setCWD = setCWD_C(f_c_string(path)) /= 0_C_INT + call system_routines_selfTest() + end function setCWD @@ -212,5 +229,30 @@ pure function f_c_string(f_string) result(c_string) end function f_c_string +!-------------------------------------------------------------------------------------------------- +!> @brief Check correctness of some system_routine functions. +!-------------------------------------------------------------------------------------------------- +subroutine system_routines_selfTest() + + real :: r + real, dimension(:), allocatable :: rnd_real + character(len=:), allocatable :: rnd_str + integer :: i + + + call random_number(r) + allocate(rnd_real(30+int(r*50.))) + call random_number(rnd_real) + allocate(character(size(rnd_real))::rnd_str) + + do i = 1, size(rnd_real) + rnd_str(i:i) = char(32 + int(rnd_real(i)*(127.-32.))) + end do + + if (c_f_string(f_c_string(rnd_str)) /= rnd_str) error stop 'c_f_string/f_c_string' + +end subroutine system_routines_selfTest + + end module system_routines diff --git a/src/test/DAMASK_test.f90 b/src/test/DAMASK_test.f90 index e2566be85..7b35174e1 100644 --- a/src/test/DAMASK_test.f90 +++ b/src/test/DAMASK_test.f90 @@ -5,6 +5,7 @@ program DAMASK_test use IO use test_prec + use test_system_routines use test_misc use test_math use test_polynomials @@ -19,7 +20,7 @@ program DAMASK_test character(len=*), parameter :: & ok = achar(27)//'[32mok'//achar(27)//'[0m', & - fmt = '(3x,a,T19,a,1x)' + fmt = '(3x,a,T20,a,1x)' call parallelization_init() call HDF5_utilities_init() @@ -34,6 +35,10 @@ program DAMASK_test call test_misc_run() write(IO_STDOUT,fmt='(a)') ok + write(IO_STDOUT,fmt=fmt, advance='no') 'system_routines','...' + call test_system_routines_run() + write(IO_STDOUT,fmt='(a)') ok + write(IO_STDOUT,fmt=fmt, advance='no') 'math','...' call test_math_run() write(IO_STDOUT,fmt='(a)') ok diff --git a/src/test/test_system_routines.f90 b/src/test/test_system_routines.f90 new file mode 100644 index 000000000..6c1996be4 --- /dev/null +++ b/src/test/test_system_routines.f90 @@ -0,0 +1,17 @@ +module test_system_routines + use system_routines + + implicit none(type,external) + + private + public :: test_system_routines_run + + contains + +subroutine test_system_routines_run() + + call system_routines_selfTest() + +end subroutine test_system_routines_run + +end module test_system_routines From 4d525c35bc6ada199601e4dd39f532c4aea6de2c Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Sat, 25 Nov 2023 16:21:00 +0100 Subject: [PATCH 60/84] use consistently case-sensitive parameters --- src/crystal.f90 | 8 ++++---- src/grid/spectral_utilities.f90 | 4 ++-- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/src/crystal.f90 b/src/crystal.f90 index f0dc1afcb..ff3291010 100644 --- a/src/crystal.f90 +++ b/src/crystal.f90 @@ -2128,9 +2128,9 @@ pure function crystal_isotropic_nu(C,assumption,lattice) result(nu) real(pREAL), dimension(6,6) :: S - if (IO_lc(assumption) == 'isostrain') then + if (assumption == 'isostrain') then K = sum(C(1:3,1:3)) / 9.0_pREAL - elseif (IO_lc(assumption) == 'isostress') then + elseif (assumption == 'isostress') then call math_invert(S,error,C) if (error) error stop 'matrix inversion failed' K = 1.0_pREAL / sum(S(1:3,1:3)) @@ -2160,7 +2160,7 @@ pure function crystal_isotropic_mu(C,assumption,lattice) result(mu) real(pREAL), dimension(6,6) :: S - if (IO_lc(assumption) == 'isostrain') then + if (assumption == 'isostrain') then select case(misc_optional(lattice,'')) case('cF','cI') mu = ( C(1,1) - C(1,2) + C(4,4)*3.0_pREAL) / 5.0_pREAL @@ -2171,7 +2171,7 @@ pure function crystal_isotropic_mu(C,assumption,lattice) result(mu) ) / 15.0_pREAL end select - elseif (IO_lc(assumption) == 'isostress') then + elseif (assumption == 'isostress') then select case(misc_optional(lattice,'')) case('cF','cI') mu = 5.0_pREAL & diff --git a/src/grid/spectral_utilities.f90 b/src/grid/spectral_utilities.f90 index de9518b8c..10915c5e4 100644 --- a/src/grid/spectral_utilities.f90 +++ b/src/grid/spectral_utilities.f90 @@ -226,7 +226,7 @@ subroutine spectral_utilities_init() scaledGeomSize = geomSize end if - select case(IO_lc(num_grid_fft%get_asStr('FFTW_plan_mode',defaultVal='FFTW_MEASURE'))) + select case(num_grid_fft%get_asStr('FFTW_plan_mode',defaultVal='FFTW_MEASURE')) case('fftw_estimate', 'FFTW_ESTIMATE') ! ordered from slow execution (but fast plan creation) to fast execution FFTW_planner_flag = FFTW_ESTIMATE case('fftw_measure', 'FFTW_MEASURE') @@ -236,7 +236,7 @@ subroutine spectral_utilities_init() case('fftw_exhaustive', 'FFTW_EXHAUSTIVE') FFTW_planner_flag = FFTW_EXHAUSTIVE case default - call IO_warning(47,'using default FFTW_MEASURE instead of "'//trim(num_grid_fft%get_asStr('plan_mode'))//'"') + call IO_warning(47,'using default FFTW_MEASURE instead of "'//trim(num_grid_fft%get_asStr('FFTW_plan_mode'))//'"') FFTW_planner_flag = FFTW_MEASURE end select From 57ee7e86e462c5221aadd7595156e45b5158ba83 Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Sun, 26 Nov 2023 07:35:17 +0100 Subject: [PATCH 61/84] better name --- src/IO.f90 | 4 ++++ src/test/test_IO.f90 | 6 +++--- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/src/IO.f90 b/src/IO.f90 index 6f34d1b7f..71d51f697 100644 --- a/src/IO.f90 +++ b/src/IO.f90 @@ -247,6 +247,7 @@ function IO_strValue(str,chunkPos,myChunk) integer, intent(in) :: myChunk !< position number of desired chunk character(len=:), allocatable :: IO_strValue + validChunk: if (myChunk > chunkPos(1) .or. myChunk < 1) then IO_strValue = '' call IO_error(110,'IO_strValue: "'//trim(str)//'"',label1='chunk',ID1=myChunk) @@ -266,6 +267,7 @@ integer function IO_intValue(str,chunkPos,myChunk) integer, dimension(:), intent(in) :: chunkPos !< positions of start and end of each tag/chunk in given string integer, intent(in) :: myChunk !< position number of desired chunk + IO_intValue = IO_strAsInt(IO_strValue(str,chunkPos,myChunk)) end function IO_intValue @@ -280,6 +282,7 @@ real(pREAL) function IO_realValue(str,chunkPos,myChunk) integer, dimension(:), intent(in) :: chunkPos !< positions of start and end of each tag/chunk in given string integer, intent(in) :: myChunk !< position number of desired chunk + IO_realValue = IO_strAsReal(IO_strValue(str,chunkPos,myChunk)) end function IO_realValue @@ -443,6 +446,7 @@ subroutine IO_error(error_ID,ext_msg,label1,ID1,label2,ID2) external :: quit character(len=:), allocatable :: msg + select case (error_ID) !-------------------------------------------------------------------------------------------------- diff --git a/src/test/test_IO.f90 b/src/test/test_IO.f90 index a5a0ac545..c1a750bc0 100644 --- a/src/test/test_IO.f90 +++ b/src/test/test_IO.f90 @@ -25,13 +25,13 @@ subroutine test_IO_run() do i = 1, size(rnd_real) rnd_str(i:i) = char(32 + int(rnd_real(i)*(127.-32.))) end do - open(newunit=u,file='results.out',status='replace',form='formatted') + open(newunit=u,file='test.txt',status='replace',form='formatted') write(u,'(a)') rnd_str close(u) - str_out = IO_read('results.out') + str_out = IO_read('test.txt') if (rnd_str//IO_EOL /= str_out) error stop 'IO_read' - strarray_out = IO_readlines('results.out') + strarray_out = IO_readlines('test.txt') if (rnd_str /= strarray_out(1)) error stop 'IO_readlines' end subroutine test_IO_run From d1432e5bd8fedc8e23494bf02ab84bd9bf91ec29 Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Sun, 26 Nov 2023 08:15:40 +0100 Subject: [PATCH 62/84] allow MPI parallel execution --- src/test/test_IO.f90 | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/src/test/test_IO.f90 b/src/test/test_IO.f90 index c1a750bc0..0ec2495a0 100644 --- a/src/test/test_IO.f90 +++ b/src/test/test_IO.f90 @@ -1,5 +1,6 @@ module test_IO use prec + use parallelization use IO implicit none(type,external) @@ -14,24 +15,25 @@ subroutine test_IO_run() real, dimension(30) :: rnd_real character(len=size(rnd_real)) :: rnd_str character(len=pSTRLEN), dimension(1) :: strarray_out - character(len=:), allocatable :: str_out + character(len=:), allocatable :: str_out, fname integer :: u,i call IO_selfTest() call random_number(rnd_real) + fname = 'test'//IO_intAsStr(worldrank)//'.txt' do i = 1, size(rnd_real) rnd_str(i:i) = char(32 + int(rnd_real(i)*(127.-32.))) end do - open(newunit=u,file='test.txt',status='replace',form='formatted') + open(newunit=u,file=fname,status='replace',form='formatted') write(u,'(a)') rnd_str close(u) - str_out = IO_read('test.txt') + str_out = IO_read(fname) if (rnd_str//IO_EOL /= str_out) error stop 'IO_read' - strarray_out = IO_readlines('test.txt') + strarray_out = IO_readlines(fname) if (rnd_str /= strarray_out(1)) error stop 'IO_readlines' end subroutine test_IO_run From 3c62af3fe5120729d1f65dd2e2f26e4f3c009ee7 Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Sun, 26 Nov 2023 08:16:24 +0100 Subject: [PATCH 63/84] test 2D arrays and read-only file opening --- src/test/test_HDF5_utilities.f90 | 30 +++++++++++++++++++++++++----- 1 file changed, 25 insertions(+), 5 deletions(-) diff --git a/src/test/test_HDF5_utilities.f90 b/src/test/test_HDF5_utilities.f90 index fb43de8e2..b4d6f39a3 100644 --- a/src/test/test_HDF5_utilities.f90 +++ b/src/test/test_HDF5_utilities.f90 @@ -20,17 +20,37 @@ end subroutine test_HDF5_utilities_run subroutine read_write() integer(HID_T) :: f - real(pREAL), dimension(3) :: d_in,d_out + real(pREAL), dimension(3) :: d1_in,d1_out + real(pREAL), dimension(3,3) :: d2_in,d2_out - call random_number(d_in) + call random_number(d1_in) + call random_number(d2_in) f = HDF5_openFile('test.hdf5','w') - call HDF5_write(d_in,f,'test') - call HDF5_read(d_out,f,'test') + call HDF5_write(d1_in,f,'d1') + call HDF5_write(d2_in,f,'d2') + + call HDF5_read(d1_out,f,'d1') + call HDF5_read(d2_out,f,'d2') + + if (any(d1_in /= d1_out)) error stop 'test_read_write(w)/d1' + if (any(d2_in /= d2_out)) error stop 'test_read_write(w)/d2' + + call HDF5_closeFile(f) + + + f = HDF5_openFile('test.hdf5','r') + + call HDF5_read(d1_out,f,'d1') + call HDF5_read(d2_out,f,'d2') + + if (any(d1_in /= d1_out)) error stop 'test_read_write(r)/d1' + if (any(d2_in /= d2_out)) error stop 'test_read_write(r)/d2' + + call HDF5_closeFile(f) - if (any(d_in /= d_out)) error stop 'test_read_write' end subroutine read_write From 1cf1f9df31d481fcae2c20ee51d416f555d2ca92 Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Sun, 26 Nov 2023 12:38:35 +0100 Subject: [PATCH 64/84] test more dimensions --- src/test/test_HDF5_utilities.f90 | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/src/test/test_HDF5_utilities.f90 b/src/test/test_HDF5_utilities.f90 index b4d6f39a3..fdab19fbc 100644 --- a/src/test/test_HDF5_utilities.f90 +++ b/src/test/test_HDF5_utilities.f90 @@ -22,21 +22,36 @@ subroutine read_write() integer(HID_T) :: f real(pREAL), dimension(3) :: d1_in,d1_out real(pREAL), dimension(3,3) :: d2_in,d2_out + real(pREAL), dimension(3,3,3) :: d3_in,d3_out + real(pREAL), dimension(3,3,3,3) :: d4_in,d4_out + real(pREAL), dimension(3,3,3,3,3) :: d5_in,d5_out call random_number(d1_in) call random_number(d2_in) + call random_number(d3_in) + call random_number(d4_in) + call random_number(d5_in) f = HDF5_openFile('test.hdf5','w') call HDF5_write(d1_in,f,'d1') call HDF5_write(d2_in,f,'d2') + call HDF5_write(d3_in,f,'d3') + call HDF5_write(d4_in,f,'d4') + call HDF5_write(d5_in,f,'d5') call HDF5_read(d1_out,f,'d1') call HDF5_read(d2_out,f,'d2') + call HDF5_read(d3_out,f,'d3') + call HDF5_read(d4_out,f,'d4') + call HDF5_read(d5_out,f,'d5') if (any(d1_in /= d1_out)) error stop 'test_read_write(w)/d1' if (any(d2_in /= d2_out)) error stop 'test_read_write(w)/d2' + if (any(d3_in /= d3_out)) error stop 'test_read_write(w)/d3' + if (any(d4_in /= d4_out)) error stop 'test_read_write(w)/d4' + if (any(d5_in /= d5_out)) error stop 'test_read_write(w)/d5' call HDF5_closeFile(f) @@ -45,9 +60,15 @@ subroutine read_write() call HDF5_read(d1_out,f,'d1') call HDF5_read(d2_out,f,'d2') + call HDF5_read(d3_out,f,'d3') + call HDF5_read(d4_out,f,'d4') + call HDF5_read(d5_out,f,'d5') if (any(d1_in /= d1_out)) error stop 'test_read_write(r)/d1' if (any(d2_in /= d2_out)) error stop 'test_read_write(r)/d2' + if (any(d3_in /= d3_out)) error stop 'test_read_write(r)/d3' + if (any(d4_in /= d4_out)) error stop 'test_read_write(r)/d4' + if (any(d5_in /= d5_out)) error stop 'test_read_write(r)/d5' call HDF5_closeFile(f) From 8bc5b30e546ff6522a6c08cacfb6fe5594539fe4 Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Sun, 26 Nov 2023 12:49:34 +0100 Subject: [PATCH 65/84] dim 6 and 7 are not used trivial do add if needed --- src/HDF5_utilities.f90 | 318 +---------------------------------------- 1 file changed, 7 insertions(+), 311 deletions(-) diff --git a/src/HDF5_utilities.f90 b/src/HDF5_utilities.f90 index 77b87c7ed..65ee66af9 100644 --- a/src/HDF5_utilities.f90 +++ b/src/HDF5_utilities.f90 @@ -38,16 +38,12 @@ module HDF5_utilities module procedure HDF5_read_real3 module procedure HDF5_read_real4 module procedure HDF5_read_real5 - module procedure HDF5_read_real6 - module procedure HDF5_read_real7 module procedure HDF5_read_int1 module procedure HDF5_read_int2 module procedure HDF5_read_int3 module procedure HDF5_read_int4 module procedure HDF5_read_int5 - module procedure HDF5_read_int6 - module procedure HDF5_read_int7 end interface HDF5_read !-------------------------------------------------------------------------------------------------- @@ -56,20 +52,19 @@ module HDF5_utilities !-------------------------------------------------------------------------------------------------- interface HDF5_write #if defined(__GFORTRAN__) + ! https://gcc.gnu.org/bugzilla/show_bug.cgi?id=105674 + ! https://gcc.gnu.org/bugzilla/show_bug.cgi?id=105687 module procedure HDF5_write_real1 module procedure HDF5_write_real2 module procedure HDF5_write_real3 module procedure HDF5_write_real4 module procedure HDF5_write_real5 - module procedure HDF5_write_real6 - module procedure HDF5_write_real7 + module procedure HDF5_write_int1 module procedure HDF5_write_int2 module procedure HDF5_write_int3 module procedure HDF5_write_int4 module procedure HDF5_write_int5 - module procedure HDF5_write_int6 - module procedure HDF5_write_int7 #else module procedure HDF5_write_real module procedure HDF5_write_int @@ -811,76 +806,6 @@ subroutine HDF5_read_real5(dataset,loc_id,datasetName,parallel) end subroutine HDF5_read_real5 -!-------------------------------------------------------------------------------------------------- -!> @brief read dataset of type real with 6 dimensions -!-------------------------------------------------------------------------------------------------- -subroutine HDF5_read_real6(dataset,loc_id,datasetName,parallel) - - real(pREAL), intent(out), dimension(:,:,:,:,:,:) :: dataset !< data read from file - integer(HID_T), intent(in) :: loc_id !< file or group handle - character(len=*), intent(in) :: datasetName !< name of the dataset in the file - logical, intent(in), optional :: parallel !< dataset is distributed over multiple processes - - integer(HID_T) :: dset_id, filespace_id, memspace_id, plist_id, aplist_id - integer(HSIZE_T), dimension(rank(dataset)) :: & - myStart, & - myShape, & !< shape of the dataset (this process) - totalShape !< shape of the dataset (all processes) - integer :: hdferr - - - myShape = int(shape(dataset),HSIZE_T) - if (any(myShape(1:size(myShape)-1) == 0)) return !< empty dataset (last dimension can be empty) - - call initialize_read(dset_id,filespace_id,memspace_id,plist_id,aplist_id, & - myStart,totalShape,loc_id,myShape,datasetName, & - misc_optional(parallel,parallel_default)) - -if (any(totalShape == 0)) return - - call H5Dread_f(dset_id, H5T_NATIVE_DOUBLE,dataset,totalShape, hdferr,& - file_space_id = filespace_id, xfer_prp = plist_id, mem_space_id = memspace_id) - call HDF5_chkerr(hdferr) - - call finalize_read(dset_id, filespace_id, memspace_id, plist_id, aplist_id) - -end subroutine HDF5_read_real6 - -!-------------------------------------------------------------------------------------------------- -!> @brief read dataset of type real with 7 dimensions -!-------------------------------------------------------------------------------------------------- -subroutine HDF5_read_real7(dataset,loc_id,datasetName,parallel) - - real(pREAL), intent(out), dimension(:,:,:,:,:,:,:) :: dataset !< data read from file - integer(HID_T), intent(in) :: loc_id !< file or group handle - character(len=*), intent(in) :: datasetName !< name of the dataset in the file - logical, intent(in), optional :: parallel !< dataset is distributed over multiple processes - - integer(HID_T) :: dset_id, filespace_id, memspace_id, plist_id, aplist_id - integer(HSIZE_T), dimension(rank(dataset)) :: & - myStart, & - myShape, & !< shape of the dataset (this process) - totalShape !< shape of the dataset (all processes) - integer :: hdferr - - - myShape = int(shape(dataset),HSIZE_T) - if (any(myShape(1:size(myShape)-1) == 0)) return !< empty dataset (last dimension can be empty) - - call initialize_read(dset_id,filespace_id,memspace_id,plist_id,aplist_id, & - myStart,totalShape,loc_id,myShape,datasetName, & - misc_optional(parallel,parallel_default)) - - if (any(totalShape == 0)) return - - call H5Dread_f(dset_id, H5T_NATIVE_DOUBLE,dataset,totalShape, hdferr,& - file_space_id = filespace_id, xfer_prp = plist_id, mem_space_id = memspace_id) - call HDF5_chkerr(hdferr) - - call finalize_read(dset_id, filespace_id, memspace_id, plist_id, aplist_id) - -end subroutine HDF5_read_real7 - !-------------------------------------------------------------------------------------------------- !> @brief read dataset of type integer with 1 dimension @@ -1053,78 +978,8 @@ subroutine HDF5_read_int5(dataset,loc_id,datasetName,parallel) end subroutine HDF5_read_int5 -!-------------------------------------------------------------------------------------------------- -!> @brief read dataset of type integer with 6 dimensions -!-------------------------------------------------------------------------------------------------- -subroutine HDF5_read_int6(dataset,loc_id,datasetName,parallel) - - integer, intent(out), dimension(:,:,:,:,:,:) :: dataset !< data read from file - integer(HID_T), intent(in) :: loc_id !< file or group handle - character(len=*), intent(in) :: datasetName !< name of the dataset in the file - logical, intent(in), optional :: parallel !< dataset is distributed over multiple processes - - integer(HID_T) :: dset_id, filespace_id, memspace_id, plist_id, aplist_id - integer(HSIZE_T), dimension(rank(dataset)) :: & - myStart, & - myShape, & !< shape of the dataset (this process) - totalShape !< shape of the dataset (all processes) - integer :: hdferr - - - myShape = int(shape(dataset),HSIZE_T) - if (any(myShape(1:size(myShape)-1) == 0)) return !< empty dataset (last dimension can be empty) - - call initialize_read(dset_id,filespace_id,memspace_id,plist_id,aplist_id, & - myStart,totalShape,loc_id,myShape,datasetName, & - misc_optional(parallel,parallel_default)) - - if (any(totalShape == 0)) return - - call H5Dread_f(dset_id, H5T_NATIVE_INTEGER,dataset,totalShape, hdferr,& - file_space_id = filespace_id, xfer_prp = plist_id, mem_space_id = memspace_id) - call HDF5_chkerr(hdferr) - - call finalize_read(dset_id, filespace_id, memspace_id, plist_id, aplist_id) - -end subroutine HDF5_read_int6 - -!-------------------------------------------------------------------------------------------------- -!> @brief read dataset of type integer with 7 dimensions -!-------------------------------------------------------------------------------------------------- -subroutine HDF5_read_int7(dataset,loc_id,datasetName,parallel) - - integer, intent(out), dimension(:,:,:,:,:,:,:) :: dataset !< data read from file - integer(HID_T), intent(in) :: loc_id !< file or group handle - character(len=*), intent(in) :: datasetName !< name of the dataset in the file - logical, intent(in), optional :: parallel !< dataset is distributed over multiple processes - - integer(HID_T) :: dset_id, filespace_id, memspace_id, plist_id, aplist_id - integer(HSIZE_T), dimension(rank(dataset)) :: & - myStart, & - myShape, & !< shape of the dataset (this process) - totalShape !< shape of the dataset (all processes) - integer :: hdferr - - - myShape = int(shape(dataset),HSIZE_T) - if (any(myShape(1:size(myShape)-1) == 0)) return !< empty dataset (last dimension can be empty) - - call initialize_read(dset_id,filespace_id,memspace_id,plist_id,aplist_id, & - myStart,totalShape,loc_id,myShape,datasetName, & - misc_optional(parallel,parallel_default)) - - if (any(totalShape == 0)) return - - call H5Dread_f(dset_id, H5T_NATIVE_INTEGER,dataset,totalShape, hdferr,& - file_space_id = filespace_id, xfer_prp = plist_id, mem_space_id = memspace_id) - call HDF5_chkerr(hdferr) - - call finalize_read(dset_id, filespace_id, memspace_id, plist_id, aplist_id) - -end subroutine HDF5_read_int7 #if defined(__GFORTRAN__) - !-------------------------------------------------------------------------------------------------- !> @brief write dataset of type real with 1 dimension !-------------------------------------------------------------------------------------------------- @@ -1311,84 +1166,10 @@ subroutine HDF5_write_real5(dataset,loc_id,datasetName,parallel) end subroutine HDF5_write_real5 -!-------------------------------------------------------------------------------------------------- -!> @brief write dataset of type real with 6 dimensions -!-------------------------------------------------------------------------------------------------- -subroutine HDF5_write_real6(dataset,loc_id,datasetName,parallel) - - real(pREAL), intent(in), dimension(:,:,:,:,:,:) :: dataset !< data written to file - integer(HID_T), intent(in) :: loc_id !< file or group handle - character(len=*), intent(in) :: datasetName !< name of the dataset in the file - logical, intent(in), optional :: parallel !< dataset is distributed over multiple processes - - - integer :: hdferr - integer(HID_T) :: dset_id, filespace_id, memspace_id, plist_id - integer(HSIZE_T), dimension(rank(dataset)) :: & - myStart, & - myShape, & !< shape of the dataset (this process) - totalShape !< shape of the dataset (all processes) - -!--------------------------------------------------------------------------------------------------- -! determine shape of dataset - myShape = int(shape(dataset),HSIZE_T) - if (any(myShape(1:size(myShape)-1) == 0)) return !< empty dataset (last dimension can be empty) - - call initialize_write(dset_id,filespace_id,memspace_id,plist_id, & - myStart,totalShape,loc_id,myShape,datasetName,H5T_NATIVE_DOUBLE, & - misc_optional(parallel,parallel_default)) - - if (product(totalShape) /= 0) then - call H5Dwrite_f(dset_id, H5T_NATIVE_DOUBLE,dataset,int(totalShape,HSIZE_T), hdferr,& - file_space_id = filespace_id, mem_space_id = memspace_id, xfer_prp = plist_id) - call HDF5_chkerr(hdferr) - end if - - call finalize_write(plist_id, dset_id, filespace_id, memspace_id) - -end subroutine HDF5_write_real6 - -!-------------------------------------------------------------------------------------------------- -!> @brief write dataset of type real with 7 dimensions -!-------------------------------------------------------------------------------------------------- -subroutine HDF5_write_real7(dataset,loc_id,datasetName,parallel) - - real(pREAL), intent(in), dimension(:,:,:,:,:,:,:) :: dataset !< data written to file - integer(HID_T), intent(in) :: loc_id !< file or group handle - character(len=*), intent(in) :: datasetName !< name of the dataset in the file - logical, intent(in), optional :: parallel !< dataset is distributed over multiple processes - - - integer :: hdferr - integer(HID_T) :: dset_id, filespace_id, memspace_id, plist_id - integer(HSIZE_T), dimension(rank(dataset)) :: & - myStart, & - myShape, & !< shape of the dataset (this process) - totalShape !< shape of the dataset (all processes) - -!--------------------------------------------------------------------------------------------------- -! determine shape of dataset - myShape = int(shape(dataset),HSIZE_T) - if (any(myShape(1:size(myShape)-1) == 0)) return !< empty dataset (last dimension can be empty) - - call initialize_write(dset_id,filespace_id,memspace_id,plist_id, & - myStart,totalShape,loc_id,myShape,datasetName,H5T_NATIVE_DOUBLE, & - misc_optional(parallel,parallel_default)) - - if (product(totalShape) /= 0) then - call H5Dwrite_f(dset_id, H5T_NATIVE_DOUBLE,dataset,int(totalShape,HSIZE_T), hdferr,& - file_space_id = filespace_id, mem_space_id = memspace_id, xfer_prp = plist_id) - call HDF5_chkerr(hdferr) - end if - - call finalize_write(plist_id, dset_id, filespace_id, memspace_id) - -end subroutine HDF5_write_real7 - #else !-------------------------------------------------------------------------------------------------- -!> @brief write dataset of type real with 1-7 dimension +!> @brief write dataset of type real with 1-5 dimension !-------------------------------------------------------------------------------------------------- subroutine HDF5_write_real(dataset,loc_id,datasetName,parallel) @@ -1431,12 +1212,6 @@ subroutine HDF5_write_real(dataset,loc_id,datasetName,parallel) rank (5) call H5Dwrite_f(dset_id, H5T_NATIVE_DOUBLE,dataset,int(totalShape,HSIZE_T), hdferr,& file_space_id = filespace_id, mem_space_id = memspace_id, xfer_prp = plist_id) - rank (6) - call H5Dwrite_f(dset_id, H5T_NATIVE_DOUBLE,dataset,int(totalShape,HSIZE_T), hdferr,& - file_space_id = filespace_id, mem_space_id = memspace_id, xfer_prp = plist_id) - rank (7) - call H5Dwrite_f(dset_id, H5T_NATIVE_DOUBLE,dataset,int(totalShape,HSIZE_T), hdferr,& - file_space_id = filespace_id, mem_space_id = memspace_id, xfer_prp = plist_id) end select call HDF5_chkerr(hdferr) end if @@ -1508,8 +1283,8 @@ subroutine HDF5_write_str(dataset,loc_id,datasetName) end subroutine HDF5_write_str -#if defined(__GFORTRAN__) +#if defined(__GFORTRAN__) !-------------------------------------------------------------------------------------------------- !> @brief Write dataset of type integer with 1 dimensions. !-------------------------------------------------------------------------------------------------- @@ -1695,84 +1470,10 @@ subroutine HDF5_write_int5(dataset,loc_id,datasetName,parallel) end subroutine HDF5_write_int5 -!-------------------------------------------------------------------------------------------------- -!> @brief Write dataset of type integer with 6 dimensions. -!-------------------------------------------------------------------------------------------------- -subroutine HDF5_write_int6(dataset,loc_id,datasetName,parallel) - - integer, intent(in), dimension(:,:,:,:,:,:) :: dataset !< data written to file - integer(HID_T), intent(in) :: loc_id !< file or group handle - character(len=*), intent(in) :: datasetName !< name of the dataset in the file - logical, intent(in), optional :: parallel !< dataset is distributed over multiple processes - - - integer :: hdferr - integer(HID_T) :: dset_id, filespace_id, memspace_id, plist_id - integer(HSIZE_T), dimension(rank(dataset)) :: & - myStart, & - myShape, & !< shape of the dataset (this process) - totalShape !< shape of the dataset (all processes) - -!--------------------------------------------------------------------------------------------------- -! determine shape of dataset - myShape = int(shape(dataset),HSIZE_T) - if (any(myShape(1:size(myShape)-1) == 0)) return !< empty dataset (last dimension can be empty) - - call initialize_write(dset_id,filespace_id,memspace_id,plist_id, & - myStart,totalShape,loc_id,myShape,datasetName,H5T_NATIVE_INTEGER, & - misc_optional(parallel,parallel_default)) - - if (product(totalShape) /= 0) then - call H5Dwrite_f(dset_id, H5T_NATIVE_INTEGER,dataset,int(totalShape,HSIZE_T), hdferr,& - file_space_id = filespace_id, mem_space_id = memspace_id, xfer_prp = plist_id) - call HDF5_chkerr(hdferr) - end if - - call finalize_write(plist_id, dset_id, filespace_id, memspace_id) - -end subroutine HDF5_write_int6 - -!-------------------------------------------------------------------------------------------------- -!> @brief Write dataset of type integer with 7 dimensions. -!-------------------------------------------------------------------------------------------------- -subroutine HDF5_write_int7(dataset,loc_id,datasetName,parallel) - - integer, intent(in), dimension(:,:,:,:,:,:,:) :: dataset !< data written to file - integer(HID_T), intent(in) :: loc_id !< file or group handle - character(len=*), intent(in) :: datasetName !< name of the dataset in the file - logical, intent(in), optional :: parallel !< dataset is distributed over multiple processes - - - integer :: hdferr - integer(HID_T) :: dset_id, filespace_id, memspace_id, plist_id - integer(HSIZE_T), dimension(rank(dataset)) :: & - myStart, & - myShape, & !< shape of the dataset (this process) - totalShape !< shape of the dataset (all processes) - -!--------------------------------------------------------------------------------------------------- -! determine shape of dataset - myShape = int(shape(dataset),HSIZE_T) - if (any(myShape(1:size(myShape)-1) == 0)) return !< empty dataset (last dimension can be empty) - - call initialize_write(dset_id,filespace_id,memspace_id,plist_id, & - myStart,totalShape,loc_id,myShape,datasetName,H5T_NATIVE_INTEGER, & - misc_optional(parallel,parallel_default)) - - if (product(totalShape) /= 0) then - call H5Dwrite_f(dset_id, H5T_NATIVE_INTEGER,dataset,int(totalShape,HSIZE_T), hdferr,& - file_space_id = filespace_id, mem_space_id = memspace_id, xfer_prp = plist_id) - call HDF5_chkerr(hdferr) - end if - - call finalize_write(plist_id, dset_id, filespace_id, memspace_id) - -end subroutine HDF5_write_int7 - #else !-------------------------------------------------------------------------------------------------- -!> @brief Write dataset of type integer with 1-7 dimensions. +!> @brief Write dataset of type integer with 1-5 dimensions. !-------------------------------------------------------------------------------------------------- subroutine HDF5_write_int(dataset,loc_id,datasetName,parallel) @@ -1815,12 +1516,6 @@ subroutine HDF5_write_int(dataset,loc_id,datasetName,parallel) rank(5) call H5Dwrite_f(dset_id, H5T_NATIVE_INTEGER,dataset,int(totalShape,HSIZE_T), hdferr,& file_space_id = filespace_id, mem_space_id = memspace_id, xfer_prp = plist_id) - rank(6) - call H5Dwrite_f(dset_id, H5T_NATIVE_INTEGER,dataset,int(totalShape,HSIZE_T), hdferr,& - file_space_id = filespace_id, mem_space_id = memspace_id, xfer_prp = plist_id) - rank(7) - call H5Dwrite_f(dset_id, H5T_NATIVE_INTEGER,dataset,int(totalShape,HSIZE_T), hdferr,& - file_space_id = filespace_id, mem_space_id = memspace_id, xfer_prp = plist_id) end select call HDF5_chkerr(hdferr) end if @@ -1830,6 +1525,7 @@ subroutine HDF5_write_int(dataset,loc_id,datasetName,parallel) end subroutine HDF5_write_int #endif + !-------------------------------------------------------------------------------------------------- !> @brief Initialize read handles and determine global shape in case of parallel IO. !-------------------------------------------------------------------------------------------------- From 246e708e5bc9ef040beeb65062d51b1ae96f4a94 Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Sun, 26 Nov 2023 13:05:39 +0100 Subject: [PATCH 66/84] systematic testing of read/write --- src/test/test_HDF5_utilities.f90 | 122 ++++++++++++++++++++++--------- 1 file changed, 86 insertions(+), 36 deletions(-) diff --git a/src/test/test_HDF5_utilities.f90 b/src/test/test_HDF5_utilities.f90 index fdab19fbc..905c9ebda 100644 --- a/src/test/test_HDF5_utilities.f90 +++ b/src/test/test_HDF5_utilities.f90 @@ -20,55 +20,105 @@ end subroutine test_HDF5_utilities_run subroutine read_write() integer(HID_T) :: f - real(pREAL), dimension(3) :: d1_in,d1_out - real(pREAL), dimension(3,3) :: d2_in,d2_out - real(pREAL), dimension(3,3,3) :: d3_in,d3_out - real(pREAL), dimension(3,3,3,3) :: d4_in,d4_out - real(pREAL), dimension(3,3,3,3,3) :: d5_in,d5_out + + real(pREAL), dimension(3) :: real_d1_in,real_d1_out + real(pREAL), dimension(3,3) :: real_d2_in,real_d2_out + real(pREAL), dimension(3,3,3) :: real_d3_in,real_d3_out + real(pREAL), dimension(3,3,3,3) :: real_d4_in,real_d4_out + real(pREAL), dimension(3,3,3,3,3) :: real_d5_in,real_d5_out + + integer, dimension(3) :: int_d1_in,int_d1_out + integer, dimension(3,3) :: int_d2_in,int_d2_out + integer, dimension(3,3,3) :: int_d3_in,int_d3_out + integer, dimension(3,3,3,3) :: int_d4_in,int_d4_out + integer, dimension(3,3,3,3,3) :: int_d5_in,int_d5_out - call random_number(d1_in) - call random_number(d2_in) - call random_number(d3_in) - call random_number(d4_in) - call random_number(d5_in) + call random_number(real_d1_in) + call random_number(real_d2_in) + call random_number(real_d3_in) + call random_number(real_d4_in) + call random_number(real_d5_in) + + int_d1_in = int(real_d1_in*2048._pREAL) + int_d2_in = int(real_d2_in*2048._pREAL) + int_d3_in = int(real_d3_in*2048._pREAL) + int_d4_in = int(real_d4_in*2048._pREAL) + int_d5_in = int(real_d5_in*2048._pREAL) + f = HDF5_openFile('test.hdf5','w') - call HDF5_write(d1_in,f,'d1') - call HDF5_write(d2_in,f,'d2') - call HDF5_write(d3_in,f,'d3') - call HDF5_write(d4_in,f,'d4') - call HDF5_write(d5_in,f,'d5') - call HDF5_read(d1_out,f,'d1') - call HDF5_read(d2_out,f,'d2') - call HDF5_read(d3_out,f,'d3') - call HDF5_read(d4_out,f,'d4') - call HDF5_read(d5_out,f,'d5') + call HDF5_write(real_d1_in,f,'real_d1') + call HDF5_write(real_d2_in,f,'real_d2') + call HDF5_write(real_d3_in,f,'real_d3') + call HDF5_write(real_d4_in,f,'real_d4') + call HDF5_write(real_d5_in,f,'real_d5') + + call HDF5_write(int_d1_in,f,'int_d1') + call HDF5_write(int_d2_in,f,'int_d2') + call HDF5_write(int_d3_in,f,'int_d3') + call HDF5_write(int_d4_in,f,'int_d4') + call HDF5_write(int_d5_in,f,'int_d5') + + + call HDF5_read(real_d1_out,f,'real_d1') + call HDF5_read(real_d2_out,f,'real_d2') + call HDF5_read(real_d3_out,f,'real_d3') + call HDF5_read(real_d4_out,f,'real_d4') + call HDF5_read(real_d5_out,f,'real_d5') + + call HDF5_read(int_d1_out,f,'int_d1') + call HDF5_read(int_d2_out,f,'int_d2') + call HDF5_read(int_d3_out,f,'int_d3') + call HDF5_read(int_d4_out,f,'int_d4') + call HDF5_read(int_d5_out,f,'int_d5') + + + if (any(real_d1_in /= real_d1_out)) error stop 'test_read_write(w)/real_d1' + if (any(real_d2_in /= real_d2_out)) error stop 'test_read_write(w)/real_d2' + if (any(real_d3_in /= real_d3_out)) error stop 'test_read_write(w)/real_d3' + if (any(real_d4_in /= real_d4_out)) error stop 'test_read_write(w)/real_d4' + if (any(real_d5_in /= real_d5_out)) error stop 'test_read_write(w)/real_d5' + + if (any(int_d1_in /= int_d1_out)) error stop 'test_read_write(w)/int_d1' + if (any(int_d2_in /= int_d2_out)) error stop 'test_read_write(w)/int_d2' + if (any(int_d3_in /= int_d3_out)) error stop 'test_read_write(w)/int_d3' + if (any(int_d4_in /= int_d4_out)) error stop 'test_read_write(w)/int_d4' + if (any(int_d5_in /= int_d5_out)) error stop 'test_read_write(w)/int_d5' - if (any(d1_in /= d1_out)) error stop 'test_read_write(w)/d1' - if (any(d2_in /= d2_out)) error stop 'test_read_write(w)/d2' - if (any(d3_in /= d3_out)) error stop 'test_read_write(w)/d3' - if (any(d4_in /= d4_out)) error stop 'test_read_write(w)/d4' - if (any(d5_in /= d5_out)) error stop 'test_read_write(w)/d5' call HDF5_closeFile(f) - f = HDF5_openFile('test.hdf5','r') - call HDF5_read(d1_out,f,'d1') - call HDF5_read(d2_out,f,'d2') - call HDF5_read(d3_out,f,'d3') - call HDF5_read(d4_out,f,'d4') - call HDF5_read(d5_out,f,'d5') - if (any(d1_in /= d1_out)) error stop 'test_read_write(r)/d1' - if (any(d2_in /= d2_out)) error stop 'test_read_write(r)/d2' - if (any(d3_in /= d3_out)) error stop 'test_read_write(r)/d3' - if (any(d4_in /= d4_out)) error stop 'test_read_write(r)/d4' - if (any(d5_in /= d5_out)) error stop 'test_read_write(r)/d5' + call HDF5_read(real_d1_out,f,'real_d1') + call HDF5_read(real_d2_out,f,'real_d2') + call HDF5_read(real_d3_out,f,'real_d3') + call HDF5_read(real_d4_out,f,'real_d4') + call HDF5_read(real_d5_out,f,'real_d5') + + call HDF5_read(int_d1_out,f,'int_d1') + call HDF5_read(int_d2_out,f,'int_d2') + call HDF5_read(int_d3_out,f,'int_d3') + call HDF5_read(int_d4_out,f,'int_d4') + call HDF5_read(int_d5_out,f,'int_d5') + + + if (any(real_d1_in /= real_d1_out)) error stop 'test_read_write(r)/real_d1' + if (any(real_d2_in /= real_d2_out)) error stop 'test_read_write(r)/real_d2' + if (any(real_d3_in /= real_d3_out)) error stop 'test_read_write(r)/real_d3' + if (any(real_d4_in /= real_d4_out)) error stop 'test_read_write(r)/real_d4' + if (any(real_d5_in /= real_d5_out)) error stop 'test_read_write(r)/real_d5' + + if (any(int_d1_in /= int_d1_out)) error stop 'test_read_write(r)/int_d1' + if (any(int_d2_in /= int_d2_out)) error stop 'test_read_write(r)/int_d2' + if (any(int_d3_in /= int_d3_out)) error stop 'test_read_write(r)/int_d3' + if (any(int_d4_in /= int_d4_out)) error stop 'test_read_write(r)/int_d4' + if (any(int_d5_in /= int_d5_out)) error stop 'test_read_write(r)/int_d5' + call HDF5_closeFile(f) From a38ec385c7879bb98c4970a4634d050aebc2e8f5 Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Sun, 26 Nov 2023 19:03:17 +0100 Subject: [PATCH 67/84] test simple tensor operations --- src/math.f90 | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/src/math.f90 b/src/math.f90 index 8d4d3ff9c..c6a358407 100644 --- a/src/math.f90 +++ b/src/math.f90 @@ -1380,6 +1380,12 @@ subroutine math_selfTest() if (any(dNeq0(math_eye(3),math_inv33(math_I3)))) & error stop 'math_inv33(math_I3)' + if (any(dNeq(t33,math_symmetric33(t33)+math_skew33(t33)))) & + error stop 'math_symmetric/skew' + + if (any(dNeq(t33,math_spherical33(t33)+math_deviatoric33(t33)))) & + error stop 'math_spherical/deviatoric' + do while(abs(math_det33(t33))<1.0e-9_pREAL) call random_number(t33) end do From 2018b623ceaad1cd074c78972e73e8a4a510ec2f Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Sun, 26 Nov 2023 19:09:34 +0100 Subject: [PATCH 68/84] wrong capitalization --- DAMASK_prerequisites.sh | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/DAMASK_prerequisites.sh b/DAMASK_prerequisites.sh index ba054dc8b..662fbbdc7 100755 --- a/DAMASK_prerequisites.sh +++ b/DAMASK_prerequisites.sh @@ -1,13 +1,13 @@ #!/usr/bin/env bash #================================================================================================== -# Execute this script (type './DAMASK_prerequisites.sh') +# Execute this script (type './DAMASK_prerequisites.sh') # and send system_report.txt to damask@mpie.de for support #================================================================================================== OUTFILE="system_report.txt" echo =========================================== -echo + Generating $OUTFILE +echo + Generating $OUTFILE echo + Send to damask@mpie.de for support echo + view with \'cat $OUTFILE\' echo =========================================== @@ -47,7 +47,7 @@ echo # redirect STDOUT and STDERR to logfile # https://stackoverflow.com/questions/11229385/redirect-all-output-in-a-bash-script-when-using-set-x^ exec > $OUTFILE 2>&1 - + # directory, file is not a symlink by definition # https://stackoverflow.com/questions/59895/getting-the-source-directory-of-a-bash-script-from-within DAMASK_ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" @@ -88,7 +88,7 @@ done secondLevel "Details on $DEFAULT_PYTHON:" echo $(ls -la $(which $DEFAULT_PYTHON)) for MODULE in numpy scipy pandas matplotlib yaml h5py;do - thirdLevel $module + thirdLevel $MODULE $DEFAULT_PYTHON -c "import $MODULE; \ print('Version: {}'.format($MODULE.__version__)); \ print('Location: {}'.format($MODULE.__file__))" From 1fb5e159adf3139aa6b5f43769fe11e29393dbbd Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Mon, 27 Nov 2023 06:39:59 +0100 Subject: [PATCH 69/84] larger tolerance (test failed on Intel) --- src/math.f90 | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/math.f90 b/src/math.f90 index c6a358407..863661c2c 100644 --- a/src/math.f90 +++ b/src/math.f90 @@ -1380,10 +1380,10 @@ subroutine math_selfTest() if (any(dNeq0(math_eye(3),math_inv33(math_I3)))) & error stop 'math_inv33(math_I3)' - if (any(dNeq(t33,math_symmetric33(t33)+math_skew33(t33)))) & + if (any(dNeq(t33,math_symmetric33(t33)+math_skew33(t33),1.0e-10_pReal))) & error stop 'math_symmetric/skew' - if (any(dNeq(t33,math_spherical33(t33)+math_deviatoric33(t33)))) & + if (any(dNeq(t33,math_spherical33(t33)+math_deviatoric33(t33),1.0e-10_pReal))) & error stop 'math_spherical/deviatoric' do while(abs(math_det33(t33))<1.0e-9_pREAL) From 558ab1c98b5e4f597a190bebe712bbd400a3ac4a Mon Sep 17 00:00:00 2001 From: Test User Date: Mon, 27 Nov 2023 15:53:09 +0100 Subject: [PATCH 70/84] [skip ci] updated version information after successful test of v3.0.0-alpha8-47-gf86d4be18 --- VERSION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/VERSION b/VERSION index 70ad5033e..9ad10d710 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -3.0.0-alpha8-34-g24864f2f2 +3.0.0-alpha8-47-gf86d4be18 From 4c5f980d00dd54fcb43b82c5bcbe143a596b5ef7 Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Mon, 27 Nov 2023 22:41:28 +0100 Subject: [PATCH 71/84] more systematic name --- PRIVATE | 2 +- processing/mentat_spectralBox.py | 2 +- python/damask/__init__.py | 2 +- python/damask/_configmaterial.py | 2 +- python/damask/{_grid.py => _geomgrid.py} | 343 +++++++++--------- python/damask/seeds.py | 6 +- .../2phase_irregularGrid.dream3d | 2 +- .../ConfigMaterial/2phase_irregularGrid.json | 2 +- .../ConfigMaterial/2phase_irregularGrid.xdmf | 2 +- .../resources/ConfigMaterial/measured.dream3d | 2 +- .../resources/ConfigMaterial/measured.xdmf | 2 +- .../{Grid => GeomGrid}/.gitattributes | 0 .../2phase_irregularGrid.dream3d | Bin .../2phase_irregularGrid.json | 0 .../2phase_irregularGrid.xdmf | 0 .../clean_1.0_1+2+3_False.vti | 0 .../clean_1.0_1+2+3_True.vti | 0 .../{Grid => GeomGrid}/clean_1.0_1_False.vti | 0 .../{Grid => GeomGrid}/clean_1.0_1_True.vti | 0 .../clean_1.0_None_False.vti | 0 .../clean_1.0_None_True.vti | 0 .../clean_1.7320508075688772_1+2+3_False.vti | 0 .../clean_1.7320508075688772_1+2+3_True.vti | 0 .../clean_1.7320508075688772_1_False.vti | 0 .../clean_1.7320508075688772_1_True.vti | 0 .../clean_1.7320508075688772_None_False.vti | 0 .../clean_1.7320508075688772_None_True.vti | 0 .../flip_directions_x-y-z.vti | 0 .../{Grid => GeomGrid}/flip_directions_x.vti | 0 .../flip_directions_y-z.vti | 0 .../flip_directions_z-x-y.vti | 0 .../get_grain_boundaries_8g12x15x20.vti | 0 ...et_grain_boundaries_8g12x15x20_x_False.vtu | 0 ...get_grain_boundaries_8g12x15x20_x_True.vtu | 0 ...t_grain_boundaries_8g12x15x20_xy_False.vtu | 0 ...et_grain_boundaries_8g12x15x20_xy_True.vtu | 0 ..._grain_boundaries_8g12x15x20_xyz_False.vtu | 0 ...t_grain_boundaries_8g12x15x20_xyz_True.vtu | 0 ...t_grain_boundaries_8g12x15x20_xz_False.vtu | 0 ...et_grain_boundaries_8g12x15x20_xz_True.vtu | 0 ...et_grain_boundaries_8g12x15x20_y_False.vtu | 0 ...get_grain_boundaries_8g12x15x20_y_True.vtu | 0 ...et_grain_boundaries_8g12x15x20_z_False.vtu | 0 ...get_grain_boundaries_8g12x15x20_z_True.vtu | 0 ...t_grain_boundaries_8g12x15x20_zy_False.vtu | 0 ...et_grain_boundaries_8g12x15x20_zy_True.vtu | 0 .../{Grid => GeomGrid}/measured.dream3d | Bin .../resources/{Grid => GeomGrid}/measured.vti | 0 .../{Grid => GeomGrid}/measured.xdmf | 0 .../mirror_directions_x+reflect_False.vti | 0 .../mirror_directions_x-y-z+reflect_True.vti | 0 .../mirror_directions_y-z+reflect_False.vti | 0 .../mirror_directions_z-x-y+reflect_False.vti | 0 .../{Grid => GeomGrid}/n10-id1_scaled.vti | 0 .../{Grid => GeomGrid}/n10-id1_scaled.vtk | Bin .../rotate_Eulers_0.0-32.0-240.0.vti | 0 .../rotate_Eulers_32.0-68.0-21.0.vti | 0 .../scale_grid_10-10-10.vti | 0 .../scale_grid_10-11-10.vti | 0 .../scale_grid_10-13-10.vti | 0 .../{Grid => GeomGrid}/scale_grid_10-20-2.vti | 0 .../{Grid => GeomGrid}/scale_grid_5-4-20.vti | 0 .../{Grid => GeomGrid}/scale_grid_8-10-12.vti | 0 python/tests/test_ConfigMaterial.py | 6 +- .../tests/{test_Grid.py => test_GeomGrid.py} | 96 ++--- python/tests/test_grid_filters.py | 4 +- python/tests/test_seeds.py | 12 +- 67 files changed, 243 insertions(+), 242 deletions(-) rename python/damask/{_grid.py => _geomgrid.py} (83%) rename python/tests/resources/{Grid => GeomGrid}/.gitattributes (100%) rename python/tests/resources/{Grid => GeomGrid}/2phase_irregularGrid.dream3d (100%) rename python/tests/resources/{Grid => GeomGrid}/2phase_irregularGrid.json (100%) rename python/tests/resources/{Grid => GeomGrid}/2phase_irregularGrid.xdmf (100%) rename python/tests/resources/{Grid => GeomGrid}/clean_1.0_1+2+3_False.vti (100%) rename python/tests/resources/{Grid => GeomGrid}/clean_1.0_1+2+3_True.vti (100%) rename python/tests/resources/{Grid => GeomGrid}/clean_1.0_1_False.vti (100%) rename python/tests/resources/{Grid => GeomGrid}/clean_1.0_1_True.vti (100%) rename python/tests/resources/{Grid => GeomGrid}/clean_1.0_None_False.vti (100%) rename python/tests/resources/{Grid => GeomGrid}/clean_1.0_None_True.vti (100%) rename python/tests/resources/{Grid => GeomGrid}/clean_1.7320508075688772_1+2+3_False.vti (100%) rename python/tests/resources/{Grid => GeomGrid}/clean_1.7320508075688772_1+2+3_True.vti (100%) rename python/tests/resources/{Grid => GeomGrid}/clean_1.7320508075688772_1_False.vti (100%) rename python/tests/resources/{Grid => GeomGrid}/clean_1.7320508075688772_1_True.vti (100%) rename python/tests/resources/{Grid => GeomGrid}/clean_1.7320508075688772_None_False.vti (100%) rename python/tests/resources/{Grid => GeomGrid}/clean_1.7320508075688772_None_True.vti (100%) rename python/tests/resources/{Grid => GeomGrid}/flip_directions_x-y-z.vti (100%) rename python/tests/resources/{Grid => GeomGrid}/flip_directions_x.vti (100%) rename python/tests/resources/{Grid => GeomGrid}/flip_directions_y-z.vti (100%) rename python/tests/resources/{Grid => GeomGrid}/flip_directions_z-x-y.vti (100%) rename python/tests/resources/{Grid => GeomGrid}/get_grain_boundaries_8g12x15x20.vti (100%) rename python/tests/resources/{Grid => GeomGrid}/get_grain_boundaries_8g12x15x20_x_False.vtu (100%) rename python/tests/resources/{Grid => GeomGrid}/get_grain_boundaries_8g12x15x20_x_True.vtu (100%) rename python/tests/resources/{Grid => GeomGrid}/get_grain_boundaries_8g12x15x20_xy_False.vtu (100%) rename python/tests/resources/{Grid => GeomGrid}/get_grain_boundaries_8g12x15x20_xy_True.vtu (100%) rename python/tests/resources/{Grid => GeomGrid}/get_grain_boundaries_8g12x15x20_xyz_False.vtu (100%) rename python/tests/resources/{Grid => GeomGrid}/get_grain_boundaries_8g12x15x20_xyz_True.vtu (100%) rename python/tests/resources/{Grid => GeomGrid}/get_grain_boundaries_8g12x15x20_xz_False.vtu (100%) rename python/tests/resources/{Grid => GeomGrid}/get_grain_boundaries_8g12x15x20_xz_True.vtu (100%) rename python/tests/resources/{Grid => GeomGrid}/get_grain_boundaries_8g12x15x20_y_False.vtu (100%) rename python/tests/resources/{Grid => GeomGrid}/get_grain_boundaries_8g12x15x20_y_True.vtu (100%) rename python/tests/resources/{Grid => GeomGrid}/get_grain_boundaries_8g12x15x20_z_False.vtu (100%) rename python/tests/resources/{Grid => GeomGrid}/get_grain_boundaries_8g12x15x20_z_True.vtu (100%) rename python/tests/resources/{Grid => GeomGrid}/get_grain_boundaries_8g12x15x20_zy_False.vtu (100%) rename python/tests/resources/{Grid => GeomGrid}/get_grain_boundaries_8g12x15x20_zy_True.vtu (100%) rename python/tests/resources/{Grid => GeomGrid}/measured.dream3d (100%) rename python/tests/resources/{Grid => GeomGrid}/measured.vti (100%) rename python/tests/resources/{Grid => GeomGrid}/measured.xdmf (100%) rename python/tests/resources/{Grid => GeomGrid}/mirror_directions_x+reflect_False.vti (100%) rename python/tests/resources/{Grid => GeomGrid}/mirror_directions_x-y-z+reflect_True.vti (100%) rename python/tests/resources/{Grid => GeomGrid}/mirror_directions_y-z+reflect_False.vti (100%) rename python/tests/resources/{Grid => GeomGrid}/mirror_directions_z-x-y+reflect_False.vti (100%) rename python/tests/resources/{Grid => GeomGrid}/n10-id1_scaled.vti (100%) rename python/tests/resources/{Grid => GeomGrid}/n10-id1_scaled.vtk (100%) rename python/tests/resources/{Grid => GeomGrid}/rotate_Eulers_0.0-32.0-240.0.vti (100%) rename python/tests/resources/{Grid => GeomGrid}/rotate_Eulers_32.0-68.0-21.0.vti (100%) rename python/tests/resources/{Grid => GeomGrid}/scale_grid_10-10-10.vti (100%) rename python/tests/resources/{Grid => GeomGrid}/scale_grid_10-11-10.vti (100%) rename python/tests/resources/{Grid => GeomGrid}/scale_grid_10-13-10.vti (100%) rename python/tests/resources/{Grid => GeomGrid}/scale_grid_10-20-2.vti (100%) rename python/tests/resources/{Grid => GeomGrid}/scale_grid_5-4-20.vti (100%) rename python/tests/resources/{Grid => GeomGrid}/scale_grid_8-10-12.vti (100%) rename python/tests/{test_Grid.py => test_GeomGrid.py} (85%) diff --git a/PRIVATE b/PRIVATE index 2cfe059d4..bc2c8e3b8 160000 --- a/PRIVATE +++ b/PRIVATE @@ -1 +1 @@ -Subproject commit 2cfe059d411b00e62e005107c3031ec988b2cd84 +Subproject commit bc2c8e3b8f405fdda0d69a2900d9b94c7cc0936f diff --git a/processing/mentat_spectralBox.py b/processing/mentat_spectralBox.py index 0c0405ce9..ccb0bfcd5 100755 --- a/processing/mentat_spectralBox.py +++ b/processing/mentat_spectralBox.py @@ -193,7 +193,7 @@ if filenames == []: filenames = [None] for name in filenames: print(script_name+': '+name) - geom = damask.Grid.load(StringIO(''.join(sys.stdin.read())) if name is None else name) + geom = damask.GeomGrid.load(StringIO(''.join(sys.stdin.read())) if name is None else name) material = geom.material.flatten(order='F') cmds = [\ diff --git a/python/damask/__init__.py b/python/damask/__init__.py index fd49a00e4..11eef8a67 100644 --- a/python/damask/__init__.py +++ b/python/damask/__init__.py @@ -26,5 +26,5 @@ from ._vtk import VTK # noqa from ._config import Config # noqa from ._configmaterial import ConfigMaterial # noqa from ._loadcasegrid import LoadcaseGrid # noqa -from ._grid import Grid # noqa +from ._geomgrid import GeomGrid # noqa from ._result import Result # noqa diff --git a/python/damask/_configmaterial.py b/python/damask/_configmaterial.py index 20037737e..299d2cc6d 100644 --- a/python/damask/_configmaterial.py +++ b/python/damask/_configmaterial.py @@ -144,7 +144,7 @@ class ConfigMaterial(Config): Notes ----- - damask.Grid.load_DREAM3D gives the corresponding geometry for + damask.GeomGrid.load_DREAM3D gives the corresponding geometry for the grid solver. For cell-wise data, only unique combinations of diff --git a/python/damask/_grid.py b/python/damask/_geomgrid.py similarity index 83% rename from python/damask/_grid.py rename to python/damask/_geomgrid.py index 935a7f767..717d42907 100644 --- a/python/damask/_grid.py +++ b/python/damask/_geomgrid.py @@ -28,7 +28,7 @@ def numba_njit_wrapper(**kwargs): return (lambda function: nb.njit(function) if nb else function) -class Grid: +class GeomGrid: """ Geometry definition for grid solvers. @@ -89,7 +89,7 @@ class Grid: ]+(['initial_conditions:']+[f' - {f}' for f in self.initial_conditions] if self.initial_conditions else [])) - def __copy__(self) -> 'Grid': + def __copy__(self) -> 'GeomGrid': """ Return deepcopy(self). @@ -110,11 +110,11 @@ class Grid: Parameters ---------- - other : damask.Grid - Grid to compare self against. + other : damask.GeomGrid + GeomGrid to compare self against. """ - if not isinstance(other, Grid): + if not isinstance(other, GeomGrid): return NotImplemented return bool( np.allclose(other.size,self.size) and np.allclose(other.origin,self.origin) @@ -197,20 +197,20 @@ class Grid: @staticmethod - def load(fname: Union[str, Path]) -> 'Grid': + def load(fname: Union[str, Path]) -> 'GeomGrid': """ Load from VTK ImageData file. Parameters ---------- fname : str or pathlib.Path - Grid file to read. + GeomGrid file to read. Valid extension is .vti, which will be appended if not given. Returns ------- - loaded : damask.Grid - Grid-based geometry from file. + loaded : damask.GeomGrid + GeomGrid-based geometry from file. """ v = VTK.load(fname if str(fname).endswith('.vti') else str(fname)+'.vti') @@ -218,17 +218,17 @@ class Grid: bbox = np.array(v.vtk_data.GetBounds()).reshape(3,2).T ic = {label:v.get(label).reshape(cells,order='F') for label in set(v.labels['Cell Data']) - {'material'}} - return Grid(material = v.get('material').reshape(cells,order='F'), - size = bbox[1] - bbox[0], - origin = bbox[0], - initial_conditions = ic, - comments = v.comments, - ) + return GeomGrid(material = v.get('material').reshape(cells,order='F'), + size = bbox[1] - bbox[0], + origin = bbox[0], + initial_conditions = ic, + comments = v.comments, + ) @typing.no_type_check @staticmethod - def load_ASCII(fname)-> 'Grid': + def load_ASCII(fname)-> 'GeomGrid': """ Load from geom file. @@ -242,8 +242,8 @@ class Grid: Returns ------- - loaded : damask.Grid - Grid-based geometry from file. + loaded : damask.GeomGrid + GeomGrid-based geometry from file. """ warnings.warn('Support for ASCII-based geom format will be removed in DAMASK 3.0.0', DeprecationWarning,2) @@ -296,14 +296,15 @@ class Grid: if not np.any(np.mod(material,1) != 0.0): # no float present material = material.astype(np.int64) - (1 if material.min() > 0 else 0) - return Grid(material = material.reshape(cells,order='F'), - size = size, - origin = origin, - comments = comments) + return GeomGrid(material = material.reshape(cells,order='F'), + size = size, + origin = origin, + comments = comments, + ) @staticmethod - def load_Neper(fname: Union[str, Path]) -> 'Grid': + def load_Neper(fname: Union[str, Path]) -> 'GeomGrid': """ Load from Neper VTK file. @@ -314,8 +315,8 @@ class Grid: Returns ------- - loaded : damask.Grid - Grid-based geometry from file. + loaded : damask.GeomGrid + GeomGrid-based geometry from file. Notes ----- @@ -330,7 +331,7 @@ class Grid: >>> N_grains = 20 >>> cells = (32,32,32) >>> damask.util.run(f'neper -T -n {N_grains} -tesrsize {cells[0]}:{cells[1]}:{cells[2]} -periodicity all -format vtk') - >>> damask.Grid.load_Neper(f'n{N_grains}-id1.vtk').renumber() + >>> damask.GeomGrid.load_Neper(f'n{N_grains}-id1.vtk').renumber() cells: 32 × 32 × 32 size: 1.0 × 1.0 × 1.0 m³ origin: 0.0 0.0 0.0 m @@ -341,11 +342,11 @@ class Grid: cells = np.array(v.vtk_data.GetDimensions())-1 bbox = np.array(v.vtk_data.GetBounds()).reshape(3,2).T - return Grid(material = v.get('MaterialId').reshape(cells,order='F').astype('int32',casting='unsafe'), - size = bbox[1] - bbox[0], - origin = bbox[0], - comments = util.execution_stamp('Grid','load_Neper'), - ) + return GeomGrid(material = v.get('MaterialId').reshape(cells,order='F').astype('int32',casting='unsafe'), + size = bbox[1] - bbox[0], + origin = bbox[0], + comments = util.execution_stamp('GeomGrid','load_Neper'), + ) @staticmethod @@ -354,7 +355,7 @@ class Grid: cell_data: Optional[str] = None, phases: str = 'Phases', Euler_angles: str = 'EulerAngles', - base_group: Optional[str] = None) -> 'Grid': + base_group: Optional[str] = None) -> 'GeomGrid': """ Load DREAM.3D (HDF5) file. @@ -389,8 +390,8 @@ class Grid: Returns ------- - loaded : damask.Grid - Grid-based geometry from file. + loaded : damask.GeomGrid + GeomGrid-based geometry from file. Notes ----- @@ -418,17 +419,17 @@ class Grid: else: ma = f['/'.join([b,c,feature_IDs])][()].flatten() - return Grid(material = ma.reshape(cells,order='F'), - size = size, - origin = origin, - comments = util.execution_stamp('Grid','load_DREAM3D'), - ) + return GeomGrid(material = ma.reshape(cells,order='F'), + size = size, + origin = origin, + comments = util.execution_stamp('GeomGrid','load_DREAM3D'), + ) @staticmethod def from_table(table: Table, coordinates: str, - labels: Union[str, Sequence[str]]) -> 'Grid': + labels: Union[str, Sequence[str]]) -> 'GeomGrid': """ Create grid from ASCII table. @@ -445,8 +446,8 @@ class Grid: Returns ------- - new : damask.Grid - Grid-based geometry from values in table. + new : damask.GeomGrid + GeomGrid-based geometry from values in table. """ cells,size,origin = grid_filters.cellsSizeOrigin_coordinates0_point(table.get(coordinates)) @@ -457,11 +458,11 @@ class Grid: ma = np.arange(cells.prod()) if len(unique) == cells.prod() else \ np.arange(unique.size)[np.argsort(pd.unique(unique_inverse))][unique_inverse] - return Grid(material = ma.reshape(cells,order='F'), - size = size, - origin = origin, - comments = util.execution_stamp('Grid','from_table'), - ) + return GeomGrid(material = ma.reshape(cells,order='F'), + size = size, + origin = origin, + comments = util.execution_stamp('GeomGrid','from_table'), + ) @staticmethod @@ -500,8 +501,8 @@ class Grid: Returns ------- - new : damask.Grid - Grid-based geometry from tessellation. + new : damask.GeomGrid + GeomGrid-based geometry from tessellation. """ weights_p: FloatSequence @@ -517,17 +518,17 @@ class Grid: coords = grid_filters.coordinates0_point(cells,size).reshape(-1,3) pool = mp.Pool(int(os.environ.get('OMP_NUM_THREADS',4))) - result = pool.map_async(partial(Grid._find_closest_seed,seeds_p,weights_p), coords) + result = pool.map_async(partial(GeomGrid._find_closest_seed,seeds_p,weights_p), coords) pool.close() pool.join() material_ = np.array(result.get()).reshape(cells) if periodic: material_ %= len(weights) - return Grid(material = material_ if material is None else np.array(material)[material_], - size = size, - comments = util.execution_stamp('Grid','from_Laguerre_tessellation'), - ) + return GeomGrid(material = material_ if material is None else np.array(material)[material_], + size = size, + comments = util.execution_stamp('GeomGrid','from_Laguerre_tessellation'), + ) @staticmethod @@ -535,7 +536,7 @@ class Grid: size: FloatSequence, seeds: np.ndarray, material: Optional[IntSequence] = None, - periodic: bool = True) -> 'Grid': + periodic: bool = True) -> 'GeomGrid': """ Create grid from Voronoi tessellation. @@ -556,8 +557,8 @@ class Grid: Returns ------- - new : damask.Grid - Grid-based geometry from tessellation. + new : damask.GeomGrid + GeomGrid-based geometry from tessellation. """ coords = grid_filters.coordinates0_point(cells,size).reshape(-1,3) @@ -568,10 +569,10 @@ class Grid: except TypeError: material_ = tree.query(coords, n_jobs = int(os.environ.get('OMP_NUM_THREADS',4)))[1] # scipy <1.6 - return Grid(material = (material_ if material is None else np.array(material)[material_]).reshape(cells), - size = size, - comments = util.execution_stamp('Grid','from_Voronoi_tessellation'), - ) + return GeomGrid(material = (material_ if material is None else np.array(material)[material_]).reshape(cells), + size = size, + comments = util.execution_stamp('GeomGrid','from_Voronoi_tessellation'), + ) _minimal_surface = \ @@ -622,7 +623,7 @@ class Grid: surface: str, threshold: float = 0.0, periods: int = 1, - materials: IntSequence = (0,1)) -> 'Grid': + materials: IntSequence = (0,1)) -> 'GeomGrid': """ Create grid from definition of triply-periodic minimal surface. @@ -643,8 +644,8 @@ class Grid: Returns ------- - new : damask.Grid - Grid-based geometry from definition of minimal surface. + new : damask.GeomGrid + GeomGrid-based geometry from definition of minimal surface. Notes ----- @@ -679,7 +680,7 @@ class Grid: >>> import numpy as np >>> import damask - >>> damask.Grid.from_minimal_surface([64]*3,np.ones(3)*1.e-4,'Gyroid') + >>> damask.GeomGrid.from_minimal_surface([64]*3,np.ones(3)*1.e-4,'Gyroid') cells : 64 × 64 × 64 size : 0.0001 × 0.0001 × 0.0001 m³ origin: 0.0 0.0 0.0 m @@ -689,7 +690,7 @@ class Grid: >>> import numpy as np >>> import damask - >>> damask.Grid.from_minimal_surface([80]*3,np.ones(3)*5.e-4, + >>> damask.GeomGrid.from_minimal_surface([80]*3,np.ones(3)*5.e-4, ... 'Neovius',materials=(1,5)) cells : 80 × 80 × 80 size : 0.0005 × 0.0005 × 0.0005 m³ @@ -701,10 +702,10 @@ class Grid: periods*2.0*np.pi*(np.arange(cells[1])+0.5)/cells[1], periods*2.0*np.pi*(np.arange(cells[2])+0.5)/cells[2], indexing='ij',sparse=True) - return Grid(material = np.where(threshold < Grid._minimal_surface[surface](x,y,z),materials[1],materials[0]), - size = size, - comments = util.execution_stamp('Grid','from_minimal_surface'), - ) + return GeomGrid(material = np.where(threshold < GeomGrid._minimal_surface[surface](x,y,z),materials[1],materials[0]), + size = size, + comments = util.execution_stamp('GeomGrid','from_minimal_surface'), + ) def save(self, @@ -781,7 +782,7 @@ class Grid: def canvas(self, cells: Optional[IntSequence] = None, offset: Optional[IntSequence] = None, - fill: Optional[int] = None) -> 'Grid': + fill: Optional[int] = None) -> 'GeomGrid': """ Crop or enlarge/pad grid. @@ -798,7 +799,7 @@ class Grid: Returns ------- - updated : damask.Grid + updated : damask.GeomGrid Updated grid-based geometry. Examples @@ -807,7 +808,7 @@ class Grid: >>> import numpy as np >>> import damask - >>> g = damask.Grid(np.zeros([32]*3,int),np.ones(3)*1e-3) + >>> g = damask.GeomGrid(np.zeros([32]*3,int),np.ones(3)*1e-3) >>> g.canvas([32,32,16],[0,0,16]) cells: 32 × 32 × 16 size: 0.001 × 0.001 × 0.0005 m³ @@ -827,16 +828,16 @@ class Grid: canvas[ll[0]:ur[0],ll[1]:ur[1],ll[2]:ur[2]] = self.material[LL[0]:UR[0],LL[1]:UR[1],LL[2]:UR[2]] - return Grid(material = canvas, - size = self.size/self.cells*np.asarray(canvas.shape), - origin = self.origin+offset_*self.size/self.cells, - comments = self.comments+[util.execution_stamp('Grid','canvas')], - ) + return GeomGrid(material = canvas, + size = self.size/self.cells*np.asarray(canvas.shape), + origin = self.origin+offset_*self.size/self.cells, + comments = self.comments+[util.execution_stamp('GeomGrid','canvas')], + ) def mirror(self, directions: Sequence[str], - reflect: bool = False) -> 'Grid': + reflect: bool = False) -> 'GeomGrid': """ Mirror grid along given directions. @@ -849,7 +850,7 @@ class Grid: Returns ------- - updated : damask.Grid + updated : damask.GeomGrid Updated grid-based geometry. Examples @@ -858,7 +859,7 @@ class Grid: >>> import numpy as np >>> import damask - >>> (g := damask.Grid(np.arange(4*5*6).reshape([4,5,6]),np.ones(3))) + >>> (g := damask.GeomGrid(np.arange(4*5*6).reshape([4,5,6]),np.ones(3))) cells: 4 × 5 × 6 size: 1.0 × 1.0 × 1.0 m³ origin: 0.0 0.0 0.0 m @@ -896,15 +897,15 @@ class Grid: if 'z' in directions: mat = np.concatenate([mat,mat[:,:,limits[0]:limits[1]:-1]],2) - return Grid(material = mat, - size = self.size/self.cells*np.asarray(mat.shape), - origin = self.origin, - comments = self.comments+[util.execution_stamp('Grid','mirror')], - ) + return GeomGrid(material = mat, + size = self.size/self.cells*np.asarray(mat.shape), + origin = self.origin, + comments = self.comments+[util.execution_stamp('GeomGrid','mirror')], + ) def flip(self, - directions: Sequence[str]) -> 'Grid': + directions: Sequence[str]) -> 'GeomGrid': """ Flip grid along given directions. @@ -915,7 +916,7 @@ class Grid: Returns ------- - updated : damask.Grid + updated : damask.GeomGrid Updated grid-based geometry. Examples @@ -924,7 +925,7 @@ class Grid: >>> import numpy as np >>> import damask - >>> (g := damask.Grid(np.arange(4*5*6).reshape([4,5,6]),np.ones(3))) + >>> (g := damask.GeomGrid(np.arange(4*5*6).reshape([4,5,6]),np.ones(3))) cells: 4 × 5 × 6 size: 1.0 × 1.0 × 1.0 m³ origin: 0.0 0.0 0.0 m @@ -943,16 +944,16 @@ class Grid: mat = np.flip(self.material, [valid.index(d) for d in directions if d in valid]) - return Grid(material = mat, - size = self.size, - origin = self.origin, - comments = self.comments+[util.execution_stamp('Grid','flip')], - ) + return GeomGrid(material = mat, + size = self.size, + origin = self.origin, + comments = self.comments+[util.execution_stamp('GeomGrid','flip')], + ) def rotate(self, R: Rotation, - fill: Optional[int] = None) -> 'Grid': + fill: Optional[int] = None) -> 'GeomGrid': """ Rotate grid (possibly extending its bounding box). @@ -966,7 +967,7 @@ class Grid: Returns ------- - updated : damask.Grid + updated : damask.GeomGrid Updated grid-based geometry. Examples @@ -975,7 +976,7 @@ class Grid: >>> import numpy as np >>> import damask - >>> (g := damask.Grid(np.arange(4*5*6).reshape([4,5,6]),np.ones(3))) + >>> (g := damask.GeomGrid(np.arange(4*5*6).reshape([4,5,6]),np.ones(3))) cells: 4 × 5 × 6 size: 1.0 × 1.0 × 1.0 m³ origin: 0.0 0.0 0.0 m @@ -997,15 +998,15 @@ class Grid: origin = self.origin-(np.asarray(material.shape)-self.cells)*.5 * self.size/self.cells - return Grid(material = material, - size = self.size/self.cells*np.asarray(material.shape), - origin = origin, - comments = self.comments+[util.execution_stamp('Grid','rotate')], - ) + return GeomGrid(material = material, + size = self.size/self.cells*np.asarray(material.shape), + origin = origin, + comments = self.comments+[util.execution_stamp('GeomGrid','rotate')], + ) def scale(self, - cells: IntSequence) -> 'Grid': + cells: IntSequence) -> 'GeomGrid': """ Scale grid to new cell counts. @@ -1016,7 +1017,7 @@ class Grid: Returns ------- - updated : damask.Grid + updated : damask.GeomGrid Updated grid-based geometry. Examples @@ -1025,7 +1026,7 @@ class Grid: >>> import numpy as np >>> import damask - >>> (g := damask.Grid(np.zeros([32]*3,int),np.ones(3)*1e-4)) + >>> (g := damask.GeomGrid(np.zeros([32]*3,int),np.ones(3)*1e-4)) cells: 32 × 32 × 32 size: 0.0001 × 0.0001 × 0.0001 m³ origin: 0.0 0.0 0.0 m @@ -1043,28 +1044,28 @@ class Grid: points=orig,method='nearest',bounds_error=False,fill_value=None) new = grid_filters.coordinates0_point(cells,self.size,self.origin) - return Grid(material = interpolator(values=self.material)(new).astype(int), - size = self.size, - origin = self.origin, - initial_conditions = {k: interpolator(values=v)(new) - for k,v in self.initial_conditions.items()}, - comments = self.comments+[util.execution_stamp('Grid','scale')], - ) + return GeomGrid(material = interpolator(values=self.material)(new).astype(int), + size = self.size, + origin = self.origin, + initial_conditions = {k: interpolator(values=v)(new) + for k,v in self.initial_conditions.items()}, + comments = self.comments+[util.execution_stamp('GeomGrid','scale')], + ) def assemble(self, - idx: np.ndarray) -> 'Grid': + idx: np.ndarray) -> 'GeomGrid': """ Assemble new grid from index map. Parameters ---------- idx : numpy.ndarray of int, shape (:,:,:) or (:,:,:,3) - Grid of flat indices or coordinate indices. + GeomGrid of flat indices or coordinate indices. Returns ------- - updated : damask.Grid + updated : damask.GeomGrid Updated grid-based geometry. Cell count of resulting grid matches shape of index map. @@ -1073,37 +1074,37 @@ class Grid: flat = (idx if len(idx.shape)==3 else grid_filters.ravel_index(idx)).flatten(order='F') ic = {k: v.flatten(order='F')[flat].reshape(cells,order='F') for k,v in self.initial_conditions.items()} - return Grid(material = self.material.flatten(order='F')[flat].reshape(cells,order='F'), - size = self.size, - origin = self.origin, - initial_conditions = ic, - comments = self.comments+[util.execution_stamp('Grid','assemble')], - ) + return GeomGrid(material = self.material.flatten(order='F')[flat].reshape(cells,order='F'), + size = self.size, + origin = self.origin, + initial_conditions = ic, + comments = self.comments+[util.execution_stamp('GeomGrid','assemble')], + ) - def renumber(self) -> 'Grid': + def renumber(self) -> 'GeomGrid': """ Renumber sorted material indices as 0,...,N-1. Returns ------- - updated : damask.Grid + updated : damask.GeomGrid Updated grid-based geometry. """ _,renumbered = np.unique(self.material,return_inverse=True) - return Grid(material = renumbered.reshape(self.cells), - size = self.size, - origin = self.origin, - initial_conditions = self.initial_conditions, - comments = self.comments+[util.execution_stamp('Grid','renumber')], - ) + return GeomGrid(material = renumbered.reshape(self.cells), + size = self.size, + origin = self.origin, + initial_conditions = self.initial_conditions, + comments = self.comments+[util.execution_stamp('GeomGrid','renumber')], + ) def substitute(self, from_material: Union[int,IntSequence], - to_material: Union[int,IntSequence]) -> 'Grid': + to_material: Union[int,IntSequence]) -> 'GeomGrid': """ Substitute material indices. @@ -1116,7 +1117,7 @@ class Grid: Returns ------- - updated : damask.Grid + updated : damask.GeomGrid Updated grid-based geometry. """ @@ -1125,21 +1126,21 @@ class Grid: to_material if isinstance(to_material,(Sequence,np.ndarray)) else [to_material]): # ToDo Python 3.10 has strict mode for zip material[self.material==f] = t - return Grid(material = material, - size = self.size, - origin = self.origin, - initial_conditions = self.initial_conditions, - comments = self.comments+[util.execution_stamp('Grid','substitute')], - ) + return GeomGrid(material = material, + size = self.size, + origin = self.origin, + initial_conditions = self.initial_conditions, + comments = self.comments+[util.execution_stamp('GeomGrid','substitute')], + ) - def sort(self) -> 'Grid': + def sort(self) -> 'GeomGrid': """ Sort material indices such that min(material ID) is located at (0,0,0). Returns ------- - updated : damask.Grid + updated : damask.GeomGrid Updated grid-based geometry. """ @@ -1148,12 +1149,12 @@ class Grid: sort_idx = np.argsort(from_ma) ma = np.unique(a)[sort_idx][np.searchsorted(from_ma,a,sorter = sort_idx)] - return Grid(material = ma.reshape(self.cells,order='F'), - size = self.size, - origin = self.origin, - initial_conditions = self.initial_conditions, - comments = self.comments+[util.execution_stamp('Grid','sort')], - ) + return GeomGrid(material = ma.reshape(self.cells,order='F'), + size = self.size, + origin = self.origin, + initial_conditions = self.initial_conditions, + comments = self.comments+[util.execution_stamp('GeomGrid','sort')], + ) def clean(self, @@ -1161,7 +1162,7 @@ class Grid: selection: Optional[IntSequence] = None, invert_selection: bool = False, periodic: bool = True, - rng_seed: Optional[NumpyRngSeed] = None) -> 'Grid': + rng_seed: Optional[NumpyRngSeed] = None) -> 'GeomGrid': """ Smooth grid by selecting most frequent material ID within given stencil at each location. @@ -1182,7 +1183,7 @@ class Grid: Returns ------- - updated : damask.Grid + updated : damask.GeomGrid Updated grid-based geometry. Notes @@ -1216,12 +1217,12 @@ class Grid: mode='wrap' if periodic else 'nearest', extra_keywords=dict(selection=selection_,rng=rng), ).astype(self.material.dtype) - return Grid(material = material, - size = self.size, - origin = self.origin, - initial_conditions = self.initial_conditions, - comments = self.comments+[util.execution_stamp('Grid','clean')], - ) + return GeomGrid(material = material, + size = self.size, + origin = self.origin, + initial_conditions = self.initial_conditions, + comments = self.comments+[util.execution_stamp('GeomGrid','clean')], + ) def add_primitive(self, @@ -1231,7 +1232,7 @@ class Grid: fill: Optional[int] = None, R: Rotation = Rotation(), inverse: bool = False, - periodic: bool = True) -> 'Grid': + periodic: bool = True) -> 'GeomGrid': """ Insert a primitive geometric object at a given position. @@ -1261,7 +1262,7 @@ class Grid: Returns ------- - updated : damask.Grid + updated : damask.GeomGrid Updated grid-based geometry. Examples @@ -1270,7 +1271,7 @@ class Grid: >>> import numpy as np >>> import damask - >>> g = damask.Grid(np.zeros([64]*3,int), np.ones(3)*1e-4) + >>> g = damask.GeomGrid(np.zeros([64]*3,int), np.ones(3)*1e-4) >>> g.add_primitive(np.ones(3)*5e-5,np.ones(3)*5e-5,1) cells : 64 × 64 × 64 size : 0.0001 × 0.0001 × 0.0001 m³ @@ -1281,7 +1282,7 @@ class Grid: >>> import numpy as np >>> import damask - >>> g = damask.Grid(np.zeros([64]*3,int), np.ones(3)*1e-4) + >>> g = damask.GeomGrid(np.zeros([64]*3,int), np.ones(3)*1e-4) >>> g.add_primitive(np.ones(3,int)*32,np.zeros(3),np.inf) cells : 64 × 64 × 64 size : 0.0001 × 0.0001 × 0.0001 m³ @@ -1307,14 +1308,14 @@ class Grid: if periodic: # translate back to center mask = np.roll(mask,((c/self.size-0.5)*self.cells).round().astype(np.int64),(0,1,2)) - return Grid(material = np.where(np.logical_not(mask) if inverse else mask, + return GeomGrid(material = np.where(np.logical_not(mask) if inverse else mask, self.material, np.nanmax(self.material)+1 if fill is None else fill), - size = self.size, - origin = self.origin, - initial_conditions = self.initial_conditions, - comments = self.comments+[util.execution_stamp('Grid','add_primitive')], - ) + size = self.size, + origin = self.origin, + initial_conditions = self.initial_conditions, + comments = self.comments+[util.execution_stamp('GeomGrid','add_primitive')], + ) def vicinity_offset(self, @@ -1322,7 +1323,7 @@ class Grid: offset: Optional[int] = None, selection: Optional[IntSequence] = None, invert_selection: bool = False, - periodic: bool = True) -> 'Grid': + periodic: bool = True) -> 'GeomGrid': """ Offset material ID of points in the vicinity of selected (or just other) material IDs. @@ -1348,7 +1349,7 @@ class Grid: Returns ------- - updated : damask.Grid + updated : damask.GeomGrid Updated grid-based geometry. """ @@ -1380,12 +1381,12 @@ class Grid: extra_keywords=dict(selection=selection_), ) - return Grid(material = np.where(mask, self.material + offset_,self.material), - size = self.size, - origin = self.origin, - initial_conditions = self.initial_conditions, - comments = self.comments+[util.execution_stamp('Grid','vicinity_offset')], - ) + return GeomGrid(material = np.where(mask, self.material + offset_,self.material), + size = self.size, + origin = self.origin, + initial_conditions = self.initial_conditions, + comments = self.comments+[util.execution_stamp('GeomGrid','vicinity_offset')], + ) def get_grain_boundaries(self, diff --git a/python/damask/seeds.py b/python/damask/seeds.py index e80425a29..85c157ece 100644 --- a/python/damask/seeds.py +++ b/python/damask/seeds.py @@ -116,7 +116,7 @@ def from_grid(grid, Parameters ---------- - grid : damask.Grid + grid : damask.GeomGrid Grid from which the material IDs are used as seeds. selection : (sequence of) int, optional Material IDs to consider. @@ -138,7 +138,7 @@ def from_grid(grid, ----- The origin is not considered in order to obtain coordinates in a coordinate system located at the origin. This is expected - by damask.Grid.from_Voronoi_tessellation. + by damask.GeomGrid.from_Voronoi_tessellation. Examples -------- @@ -148,7 +148,7 @@ def from_grid(grid, >>> import scipy.spatial >>> import damask >>> seeds = damask.seeds.from_random(np.ones(3),29,[128]*3) - >>> (g := damask.Grid.from_Voronoi_tessellation([128]*3,np.ones(3),seeds)) + >>> (g := damask.GeomGrid.from_Voronoi_tessellation([128]*3,np.ones(3),seeds)) cells: 128 × 128 × 128 size: 1.0 × 1.0 × 1.0 m³ origin: 0.0 0.0 0.0 m diff --git a/python/tests/resources/ConfigMaterial/2phase_irregularGrid.dream3d b/python/tests/resources/ConfigMaterial/2phase_irregularGrid.dream3d index 94f337206..0c1611d87 120000 --- a/python/tests/resources/ConfigMaterial/2phase_irregularGrid.dream3d +++ b/python/tests/resources/ConfigMaterial/2phase_irregularGrid.dream3d @@ -1 +1 @@ -../Grid/2phase_irregularGrid.dream3d \ No newline at end of file +../GeomGrid/2phase_irregularGrid.dream3d \ No newline at end of file diff --git a/python/tests/resources/ConfigMaterial/2phase_irregularGrid.json b/python/tests/resources/ConfigMaterial/2phase_irregularGrid.json index 66071ae27..893be73a4 120000 --- a/python/tests/resources/ConfigMaterial/2phase_irregularGrid.json +++ b/python/tests/resources/ConfigMaterial/2phase_irregularGrid.json @@ -1 +1 @@ -../Grid/2phase_irregularGrid.json \ No newline at end of file +../GeomGrid/2phase_irregularGrid.json \ No newline at end of file diff --git a/python/tests/resources/ConfigMaterial/2phase_irregularGrid.xdmf b/python/tests/resources/ConfigMaterial/2phase_irregularGrid.xdmf index 3736e65db..be0be47ca 120000 --- a/python/tests/resources/ConfigMaterial/2phase_irregularGrid.xdmf +++ b/python/tests/resources/ConfigMaterial/2phase_irregularGrid.xdmf @@ -1 +1 @@ -../Grid/2phase_irregularGrid.xdmf \ No newline at end of file +../GeomGrid/2phase_irregularGrid.xdmf \ No newline at end of file diff --git a/python/tests/resources/ConfigMaterial/measured.dream3d b/python/tests/resources/ConfigMaterial/measured.dream3d index 51ed3e080..fd10b7be0 120000 --- a/python/tests/resources/ConfigMaterial/measured.dream3d +++ b/python/tests/resources/ConfigMaterial/measured.dream3d @@ -1 +1 @@ -../Grid/measured.dream3d \ No newline at end of file +../GeomGrid/measured.dream3d \ No newline at end of file diff --git a/python/tests/resources/ConfigMaterial/measured.xdmf b/python/tests/resources/ConfigMaterial/measured.xdmf index a6603a37a..1231f59ac 120000 --- a/python/tests/resources/ConfigMaterial/measured.xdmf +++ b/python/tests/resources/ConfigMaterial/measured.xdmf @@ -1 +1 @@ -../Grid/measured.xdmf \ No newline at end of file +../GeomGrid/measured.xdmf \ No newline at end of file diff --git a/python/tests/resources/Grid/.gitattributes b/python/tests/resources/GeomGrid/.gitattributes similarity index 100% rename from python/tests/resources/Grid/.gitattributes rename to python/tests/resources/GeomGrid/.gitattributes diff --git a/python/tests/resources/Grid/2phase_irregularGrid.dream3d 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python/tests/resources/GeomGrid/get_grain_boundaries_8g12x15x20_zy_True.vtu diff --git a/python/tests/resources/Grid/measured.dream3d b/python/tests/resources/GeomGrid/measured.dream3d similarity index 100% rename from python/tests/resources/Grid/measured.dream3d rename to python/tests/resources/GeomGrid/measured.dream3d diff --git a/python/tests/resources/Grid/measured.vti b/python/tests/resources/GeomGrid/measured.vti similarity index 100% rename from python/tests/resources/Grid/measured.vti rename to python/tests/resources/GeomGrid/measured.vti diff --git a/python/tests/resources/Grid/measured.xdmf b/python/tests/resources/GeomGrid/measured.xdmf similarity index 100% rename from python/tests/resources/Grid/measured.xdmf rename to python/tests/resources/GeomGrid/measured.xdmf diff --git a/python/tests/resources/Grid/mirror_directions_x+reflect_False.vti b/python/tests/resources/GeomGrid/mirror_directions_x+reflect_False.vti similarity index 100% rename from python/tests/resources/Grid/mirror_directions_x+reflect_False.vti rename to python/tests/resources/GeomGrid/mirror_directions_x+reflect_False.vti diff --git a/python/tests/resources/Grid/mirror_directions_x-y-z+reflect_True.vti b/python/tests/resources/GeomGrid/mirror_directions_x-y-z+reflect_True.vti similarity index 100% rename from python/tests/resources/Grid/mirror_directions_x-y-z+reflect_True.vti rename to python/tests/resources/GeomGrid/mirror_directions_x-y-z+reflect_True.vti diff --git a/python/tests/resources/Grid/mirror_directions_y-z+reflect_False.vti b/python/tests/resources/GeomGrid/mirror_directions_y-z+reflect_False.vti similarity index 100% rename from python/tests/resources/Grid/mirror_directions_y-z+reflect_False.vti rename to python/tests/resources/GeomGrid/mirror_directions_y-z+reflect_False.vti diff --git a/python/tests/resources/Grid/mirror_directions_z-x-y+reflect_False.vti b/python/tests/resources/GeomGrid/mirror_directions_z-x-y+reflect_False.vti similarity index 100% rename from python/tests/resources/Grid/mirror_directions_z-x-y+reflect_False.vti rename to python/tests/resources/GeomGrid/mirror_directions_z-x-y+reflect_False.vti diff --git a/python/tests/resources/Grid/n10-id1_scaled.vti b/python/tests/resources/GeomGrid/n10-id1_scaled.vti similarity index 100% rename from python/tests/resources/Grid/n10-id1_scaled.vti rename to python/tests/resources/GeomGrid/n10-id1_scaled.vti diff --git a/python/tests/resources/Grid/n10-id1_scaled.vtk b/python/tests/resources/GeomGrid/n10-id1_scaled.vtk similarity index 100% rename from python/tests/resources/Grid/n10-id1_scaled.vtk rename to python/tests/resources/GeomGrid/n10-id1_scaled.vtk diff --git a/python/tests/resources/Grid/rotate_Eulers_0.0-32.0-240.0.vti b/python/tests/resources/GeomGrid/rotate_Eulers_0.0-32.0-240.0.vti similarity index 100% rename from python/tests/resources/Grid/rotate_Eulers_0.0-32.0-240.0.vti rename to python/tests/resources/GeomGrid/rotate_Eulers_0.0-32.0-240.0.vti diff --git a/python/tests/resources/Grid/rotate_Eulers_32.0-68.0-21.0.vti b/python/tests/resources/GeomGrid/rotate_Eulers_32.0-68.0-21.0.vti similarity index 100% rename from python/tests/resources/Grid/rotate_Eulers_32.0-68.0-21.0.vti rename to python/tests/resources/GeomGrid/rotate_Eulers_32.0-68.0-21.0.vti diff --git a/python/tests/resources/Grid/scale_grid_10-10-10.vti b/python/tests/resources/GeomGrid/scale_grid_10-10-10.vti similarity index 100% rename from python/tests/resources/Grid/scale_grid_10-10-10.vti rename to python/tests/resources/GeomGrid/scale_grid_10-10-10.vti diff --git a/python/tests/resources/Grid/scale_grid_10-11-10.vti b/python/tests/resources/GeomGrid/scale_grid_10-11-10.vti similarity index 100% rename from python/tests/resources/Grid/scale_grid_10-11-10.vti rename to python/tests/resources/GeomGrid/scale_grid_10-11-10.vti diff --git a/python/tests/resources/Grid/scale_grid_10-13-10.vti b/python/tests/resources/GeomGrid/scale_grid_10-13-10.vti similarity index 100% rename from python/tests/resources/Grid/scale_grid_10-13-10.vti rename to python/tests/resources/GeomGrid/scale_grid_10-13-10.vti diff --git a/python/tests/resources/Grid/scale_grid_10-20-2.vti b/python/tests/resources/GeomGrid/scale_grid_10-20-2.vti similarity index 100% rename from python/tests/resources/Grid/scale_grid_10-20-2.vti rename to python/tests/resources/GeomGrid/scale_grid_10-20-2.vti diff --git a/python/tests/resources/Grid/scale_grid_5-4-20.vti b/python/tests/resources/GeomGrid/scale_grid_5-4-20.vti similarity index 100% rename from python/tests/resources/Grid/scale_grid_5-4-20.vti rename to python/tests/resources/GeomGrid/scale_grid_5-4-20.vti diff --git a/python/tests/resources/Grid/scale_grid_8-10-12.vti b/python/tests/resources/GeomGrid/scale_grid_8-10-12.vti similarity index 100% rename from python/tests/resources/Grid/scale_grid_8-10-12.vti rename to python/tests/resources/GeomGrid/scale_grid_8-10-12.vti diff --git a/python/tests/test_ConfigMaterial.py b/python/tests/test_ConfigMaterial.py index b350356a8..f752a070a 100644 --- a/python/tests/test_ConfigMaterial.py +++ b/python/tests/test_ConfigMaterial.py @@ -6,7 +6,7 @@ import numpy as np from damask import ConfigMaterial from damask import Table from damask import Rotation -from damask import Grid +from damask import GeomGrid @pytest.fixture def res_path(res_path_base): @@ -182,8 +182,8 @@ class TestConfigMaterial: assert point_c.is_valid and grain_c.is_valid and \ len(point_c['material'])+1 == len(grain_c['material']) - grain_m = Grid.load_DREAM3D(res_path/'2phase_irregularGrid.dream3d','FeatureIds').material.flatten() - point_m = Grid.load_DREAM3D(res_path/'2phase_irregularGrid.dream3d').material.flatten() + grain_m = GeomGrid.load_DREAM3D(res_path/'2phase_irregularGrid.dream3d','FeatureIds').material.flatten() + point_m = GeomGrid.load_DREAM3D(res_path/'2phase_irregularGrid.dream3d').material.flatten() for i in np.unique(point_m): j = int(grain_m[(point_m==i).nonzero()[0][0]]) diff --git a/python/tests/test_Grid.py b/python/tests/test_GeomGrid.py similarity index 85% rename from python/tests/test_Grid.py rename to python/tests/test_GeomGrid.py index 237ea32e5..f09ba07bf 100644 --- a/python/tests/test_Grid.py +++ b/python/tests/test_GeomGrid.py @@ -5,7 +5,7 @@ import numpy as np from vtkmodules.vtkCommonCore import vtkVersion from damask import VTK -from damask import Grid +from damask import GeomGrid from damask import Table from damask import Rotation from damask import Colormap @@ -19,7 +19,7 @@ def default(): """Simple geometry.""" g = np.array([8,5,4]) l = np.prod(g[:2]) - return Grid(np.concatenate((np.ones (l,dtype=int), + return GeomGrid(np.concatenate((np.ones (l,dtype=int), np.arange(l,dtype=int)+2, np.ones (l,dtype=int)*2, np.arange(l,dtype=int)+1)).reshape(g,order='F'), @@ -31,15 +31,15 @@ def random(): size = (1+np.random.rand(3))*1e-5 cells = np.random.randint(10,20,3) s = seeds.from_random(size,np.random.randint(5,25),cells) - return Grid.from_Voronoi_tessellation(cells,size,s) + return GeomGrid.from_Voronoi_tessellation(cells,size,s) @pytest.fixture def res_path(res_path_base): """Directory containing testing resources.""" - return res_path_base/'Grid' + return res_path_base/'GeomGrid' -class TestGrid: +class TestGeomGrid: @pytest.fixture(autouse=True) def _patch_execution_stamp(self, patch_execution_stamp): @@ -65,49 +65,49 @@ class TestGrid: def test_read_write_vti(self,default,tmp_path): default.save(tmp_path/'default') - new = Grid.load(tmp_path/'default.vti') + new = GeomGrid.load(tmp_path/'default.vti') assert new == default def test_invalid_no_material(self,tmp_path): v = VTK.from_image_data(np.random.randint(5,10,3)*2,np.random.random(3) + 1.0) v.save(tmp_path/'no_materialpoint.vti',parallel=False) with pytest.raises(KeyError): - Grid.load(tmp_path/'no_materialpoint.vti') + GeomGrid.load(tmp_path/'no_materialpoint.vti') def test_invalid_material_type(self): with pytest.raises(TypeError): - Grid(np.zeros((3,3,3),dtype='complex'),np.ones(3)) + GeomGrid(np.zeros((3,3,3),dtype='complex'),np.ones(3)) def test_cast_to_int(self): - g = Grid(np.zeros((3,3,3)),np.ones(3)) + g = GeomGrid(np.zeros((3,3,3)),np.ones(3)) assert g.material.dtype in np.sctypes['int'] def test_invalid_size(self,default): with pytest.raises(ValueError): - Grid(default.material[1:,1:,1:], + GeomGrid(default.material[1:,1:,1:], size=np.ones(2)) def test_save_load_ASCII(self,default,tmp_path): default.save_ASCII(tmp_path/'ASCII') default.material -= 1 - assert Grid.load_ASCII(tmp_path/'ASCII') == default + assert GeomGrid.load_ASCII(tmp_path/'ASCII') == default def test_invalid_origin(self,default): with pytest.raises(ValueError): - Grid(default.material[1:,1:,1:], + GeomGrid(default.material[1:,1:,1:], size=np.ones(3), origin=np.ones(4)) def test_invalid_materials_shape(self,default): material = np.ones((3,3)) with pytest.raises(ValueError): - Grid(material, + GeomGrid(material, size=np.ones(3)) def test_invalid_materials_type(self,default): material = np.random.randint(1,300,(3,4,5))==1 with pytest.raises(TypeError): - Grid(material) + GeomGrid(material) @pytest.mark.parametrize('directions,reflect',[ (['x'], False), @@ -121,7 +121,7 @@ class TestGrid: tag = f'directions_{"-".join(directions)}+reflect_{reflect}' reference = res_path/f'mirror_{tag}.vti' if update: modified.save(reference) - assert Grid.load(reference) == modified + assert GeomGrid.load(reference) == modified @pytest.mark.parametrize('directions',[(1,2,'y'),('a','b','x'),[1]]) def test_mirror_invalid(self,default,directions): @@ -146,7 +146,7 @@ class TestGrid: tag = f'directions_{"-".join(directions)}' reference = res_path/f'flip_{tag}.vti' if update: modified.save(reference) - assert Grid.load(reference) == modified + assert GeomGrid.load(reference) == modified def test_flip_order_invariant(self,default): direction = np.array(['x','y','z']) @@ -180,7 +180,7 @@ class TestGrid: reference = res_path/f'clean_{distance}_{util.srepr(selection,"+")}_{periodic}.vti' if update: current.save(reference) - assert Grid.load(reference) == current + assert GeomGrid.load(reference) == current @pytest.mark.parametrize('selection',[list(np.random.randint(1,20,6)),np.random.randint(1,20,6)]) @pytest.mark.parametrize('invert',[True,False]) @@ -207,14 +207,14 @@ class TestGrid: tag = f'grid_{util.srepr(cells,"-")}' reference = res_path/f'scale_{tag}.vti' if update: modified.save(reference) - assert Grid.load(reference) == modified + assert GeomGrid.load(reference) == modified def test_renumber(self,default): material = default.material.copy() for m in np.unique(material): material[material==m] = material.max() + np.random.randint(1,30) default.material -= 1 - modified = Grid(material, + modified = GeomGrid(material, default.size, default.origin) assert not default == modified @@ -223,13 +223,13 @@ class TestGrid: def test_assemble(self): cells = np.random.randint(8,16,3) N = cells.prod() - g = Grid(np.arange(N).reshape(cells),np.ones(3)) + g = GeomGrid(np.arange(N).reshape(cells),np.ones(3)) idx = np.random.randint(0,N,N).reshape(cells) assert (idx == g.assemble(idx).material).all def test_substitute(self,default): offset = np.random.randint(1,500) - modified = Grid(default.material + offset, + modified = GeomGrid(default.material + offset, default.size, default.origin) assert not default == modified @@ -250,7 +250,7 @@ class TestGrid: def test_sort(self): cells = np.random.randint(5,20,3) - m = Grid(np.random.randint(1,20,cells)*3,np.ones(3)).sort().material.flatten(order='F') + m = GeomGrid(np.random.randint(1,20,cells)*3,np.ones(3)).sort().material.flatten(order='F') for i,v in enumerate(m): assert i==0 or v > m[:i].max() or v in m[:i] @@ -269,7 +269,7 @@ class TestGrid: tag = f'Eulers_{util.srepr(Eulers,"-")}' reference = res_path/f'rotate_{tag}.vti' if update: modified.save(reference) - assert Grid.load(reference) == modified + assert GeomGrid.load(reference) == modified def test_canvas_extend(self,default): cells = default.cells @@ -280,7 +280,7 @@ class TestGrid: @pytest.mark.parametrize('sign',[+1,-1]) @pytest.mark.parametrize('extra_offset',[0,-1]) def test_canvas_move_out(self,sign,extra_offset): - g = Grid(np.zeros(np.random.randint(3,30,(3)),int),np.ones(3)) + g = GeomGrid(np.zeros(np.random.randint(3,30,(3)),int),np.ones(3)) o = sign*np.ones(3)*g.cells.min() +extra_offset*sign if extra_offset == 0: assert np.all(g.canvas(offset=o).material == 1) @@ -288,7 +288,7 @@ class TestGrid: assert np.all(np.unique(g.canvas(offset=o).material) == (0,1)) def test_canvas_cells(self,default): - g = Grid(np.zeros(np.random.randint(3,30,(3)),int),np.ones(3)) + g = GeomGrid(np.zeros(np.random.randint(3,30,(3)),int),np.ones(3)) cells = np.random.randint(1,30,(3)) offset = np.random.randint(-30,30,(3)) assert np.all(g.canvas(cells,offset).cells == cells) @@ -308,8 +308,8 @@ class TestGrid: o = np.random.random(3)-.5 g = np.random.randint(8,32,(3)) s = np.random.random(3)+.5 - G_1 = Grid(np.ones(g,'i'),s,o).add_primitive(diameter,center1,exponent) - G_2 = Grid(np.ones(g,'i'),s,o).add_primitive(diameter,center2,exponent) + G_1 = GeomGrid(np.ones(g,'i'),s,o).add_primitive(diameter,center1,exponent) + G_2 = GeomGrid(np.ones(g,'i'),s,o).add_primitive(diameter,center2,exponent) assert np.count_nonzero(G_1.material!=2) == np.count_nonzero(G_2.material!=2) @pytest.mark.parametrize('center',[np.random.randint(4,10,(3)), @@ -323,8 +323,8 @@ class TestGrid: g = np.random.randint(8,32,(3)) s = np.random.random(3)+.5 fill = np.random.randint(10)+2 - G_1 = Grid(np.ones(g,'i'),s).add_primitive(.3,center,1,fill,inverse=inverse,periodic=periodic) - G_2 = Grid(np.ones(g,'i'),s).add_primitive(.3,center,1,fill,Rotation.from_random(),inverse,periodic=periodic) + G_1 = GeomGrid(np.ones(g,'i'),s).add_primitive(.3,center,1,fill,inverse=inverse,periodic=periodic) + G_2 = GeomGrid(np.ones(g,'i'),s).add_primitive(.3,center,1,fill,Rotation.from_random(),inverse,periodic=periodic) assert G_1 == G_2 @pytest.mark.parametrize('exponent',[1,np.inf,np.random.random(3)*2.]) @@ -332,7 +332,7 @@ class TestGrid: """Shapes defined in the center should always produce a grid with reflection symmetry along the coordinate axis.""" o = np.random.random(3)-.5 s = np.random.random(3)*5. - grid = Grid(np.zeros(np.random.randint(8,32,3),'i'),s,o).add_primitive(np.random.random(3)*3.,o+s/2.,exponent) + grid = GeomGrid(np.zeros(np.random.randint(8,32,3),'i'),s,o).add_primitive(np.random.random(3)*3.,o+s/2.,exponent) for axis in [0,1,2]: assert np.all(grid.material==np.flip(grid.material,axis=axis)) @@ -352,7 +352,7 @@ class TestGrid: if selection == 1: m2[m==1] = 1 - grid = Grid(m,np.random.rand(3)).vicinity_offset(distance,offset,selection=selection) + grid = GeomGrid(m,np.random.rand(3)).vicinity_offset(distance,offset,selection=selection) assert np.all(m2==grid.material) @@ -379,8 +379,8 @@ class TestGrid: size = np.random.random(3) + 1.0 N_seeds= np.random.randint(10,30) seeds = np.random.rand(N_seeds,3) * np.broadcast_to(size,(N_seeds,3)) - Voronoi = Grid.from_Voronoi_tessellation( cells,size,seeds, np.arange(N_seeds)+5,periodic) - Laguerre = Grid.from_Laguerre_tessellation(cells,size,seeds,np.ones(N_seeds),np.arange(N_seeds)+5,periodic) + Voronoi = GeomGrid.from_Voronoi_tessellation( cells,size,seeds, np.arange(N_seeds)+5,periodic) + Laguerre = GeomGrid.from_Laguerre_tessellation(cells,size,seeds,np.ones(N_seeds),np.arange(N_seeds)+5,periodic) assert Laguerre == Voronoi def test_Laguerre_weights(self): @@ -391,7 +391,7 @@ class TestGrid: weights= np.full((N_seeds),-np.inf) ms = np.random.randint(N_seeds) weights[ms] = np.random.random() - Laguerre = Grid.from_Laguerre_tessellation(cells,size,seeds,weights,periodic=np.random.random()>0.5) + Laguerre = GeomGrid.from_Laguerre_tessellation(cells,size,seeds,weights,periodic=np.random.random()>0.5) assert np.all(Laguerre.material == ms) @pytest.mark.parametrize('approach',['Laguerre','Voronoi']) @@ -402,9 +402,9 @@ class TestGrid: material = np.zeros(cells) material[:,cells[1]//2:,:] = 1 if approach == 'Laguerre': - grid = Grid.from_Laguerre_tessellation(cells,size,seeds,np.ones(2),periodic=np.random.random()>0.5) + grid = GeomGrid.from_Laguerre_tessellation(cells,size,seeds,np.ones(2),periodic=np.random.random()>0.5) elif approach == 'Voronoi': - grid = Grid.from_Voronoi_tessellation(cells,size,seeds, periodic=np.random.random()>0.5) + grid = GeomGrid.from_Voronoi_tessellation(cells,size,seeds, periodic=np.random.random()>0.5) assert np.all(grid.material == material) @pytest.mark.parametrize('surface',['Schwarz P', @@ -426,7 +426,7 @@ class TestGrid: threshold = 2*np.random.rand()-1. periods = np.random.randint(2)+1 materials = np.random.randint(0,40,2) - grid = Grid.from_minimal_surface(cells,size,surface,threshold,periods,materials) + grid = GeomGrid.from_minimal_surface(cells,size,surface,threshold,periods,materials) assert set(grid.material.flatten()) | set(materials) == set(materials) \ and (grid.size == size).all() and (grid.cells == cells).all() @@ -445,7 +445,7 @@ class TestGrid: ]) def test_minimal_surface_volume(self,surface,threshold): cells = np.ones(3,dtype=int)*64 - grid = Grid.from_minimal_surface(cells,np.ones(3),surface,threshold) + grid = GeomGrid.from_minimal_surface(cells,np.ones(3),surface,threshold) assert np.isclose(np.count_nonzero(grid.material==1)/np.prod(grid.cells),.5,rtol=1e-3) def test_from_table(self): @@ -456,23 +456,23 @@ class TestGrid: z[cells[:2].prod()*int(cells[2]/2):] = 0 t = Table({'coords':3,'z':1},np.column_stack((coords,z))) t = t.set('indicator',t.get('coords')[:,0]) - g = Grid.from_table(t,'coords',['indicator','z']) + g = GeomGrid.from_table(t,'coords',['indicator','z']) assert g.N_materials == g.cells[0]*2 and (g.material[:,:,-1]-g.material[:,:,0] == cells[0]).all() def test_from_table_recover(self,tmp_path): cells = np.random.randint(60,100,3) size = np.ones(3)+np.random.rand(3) s = seeds.from_random(size,np.random.randint(60,100)) - grid = Grid.from_Voronoi_tessellation(cells,size,s) + grid = GeomGrid.from_Voronoi_tessellation(cells,size,s) coords = grid_filters.coordinates0_point(cells,size) t = Table({'c':3,'m':1},np.column_stack((coords.reshape(-1,3,order='F'),grid.material.flatten(order='F')))) - assert grid.sort().renumber() == Grid.from_table(t,'c',['m']) + assert grid.sort().renumber() == GeomGrid.from_table(t,'c',['m']) @pytest.mark.parametrize('periodic',[True,False]) @pytest.mark.parametrize('direction',['x','y','z',['x','y'],'zy','xz',['x','y','z']]) @pytest.mark.xfail(vtkVersion.GetVTKMajorVersion()<8, reason='missing METADATA') def test_get_grain_boundaries(self,update,res_path,periodic,direction): - grid = Grid.load(res_path/'get_grain_boundaries_8g12x15x20.vti') + grid = GeomGrid.load(res_path/'get_grain_boundaries_8g12x15x20.vti') current = grid.get_grain_boundaries(periodic,direction) if update: current.save(res_path/f'get_grain_boundaries_8g12x15x20_{direction}_{periodic}.vtu',parallel=False) @@ -485,24 +485,24 @@ class TestGrid: default.get_grain_boundaries(directions=directions) def test_load_DREAM3D(self,res_path): - grain = Grid.load_DREAM3D(res_path/'2phase_irregularGrid.dream3d','FeatureIds') - point = Grid.load_DREAM3D(res_path/'2phase_irregularGrid.dream3d') + grain = GeomGrid.load_DREAM3D(res_path/'2phase_irregularGrid.dream3d','FeatureIds') + point = GeomGrid.load_DREAM3D(res_path/'2phase_irregularGrid.dream3d') assert np.allclose(grain.origin,point.origin) and \ np.allclose(grain.size,point.size) and \ (grain.sort().material == point.material+1).all() def test_load_DREAM3D_reference(self,res_path,update): - current = Grid.load_DREAM3D(res_path/'measured.dream3d') - reference = Grid.load(res_path/'measured.vti') + current = GeomGrid.load_DREAM3D(res_path/'measured.dream3d') + reference = GeomGrid.load(res_path/'measured.vti') if update: current.save(res_path/'measured.vti') assert current == reference def test_load_Neper_reference(self,res_path,update): - current = Grid.load_Neper(res_path/'n10-id1_scaled.vtk').renumber() - reference = Grid.load(res_path/'n10-id1_scaled.vti') + current = GeomGrid.load_Neper(res_path/'n10-id1_scaled.vtk').renumber() + reference = GeomGrid.load(res_path/'n10-id1_scaled.vti') if update: current.save(res_path/'n10-id1_scaled.vti') diff --git a/python/tests/test_grid_filters.py b/python/tests/test_grid_filters.py index d19bf03e9..93b9a531b 100644 --- a/python/tests/test_grid_filters.py +++ b/python/tests/test_grid_filters.py @@ -3,8 +3,8 @@ import numpy as np from damask import grid_filters from damask import mechanics -from damask import Grid from damask import seeds +from damask import GeomGrid class TestGridFilters: @@ -205,7 +205,7 @@ class TestGridFilters: def test_regrid_double_cells(self): size = np.random.random(3) # noqa cells = np.random.randint(8,32,(3)) - g = Grid.from_Voronoi_tessellation(cells,size,seeds.from_random(size,10)) + g = GeomGrid.from_Voronoi_tessellation(cells,size,seeds.from_random(size,10)) F = np.broadcast_to(np.eye(3), (*cells,3,3)) assert g.scale(cells*2) == g.assemble(grid_filters.regrid(size,F,cells*2)) diff --git a/python/tests/test_seeds.py b/python/tests/test_seeds.py index ca5cd6f6d..379684613 100644 --- a/python/tests/test_seeds.py +++ b/python/tests/test_seeds.py @@ -4,7 +4,7 @@ from scipy.spatial import cKDTree from damask import seeds from damask import grid_filters -from damask import Grid +from damask import GeomGrid class TestSeeds: @@ -40,9 +40,9 @@ class TestSeeds: N_seeds = np.random.randint(30,300) size = np.ones(3) + np.random.random(3) coords = seeds.from_random(size,N_seeds,cells) - grid_1 = Grid.from_Voronoi_tessellation(cells,size,coords) + grid_1 = GeomGrid.from_Voronoi_tessellation(cells,size,coords) coords,material = seeds.from_grid(grid_1) - grid_2 = Grid.from_Voronoi_tessellation(cells,size,coords,material) + grid_2 = GeomGrid.from_Voronoi_tessellation(cells,size,coords,material) assert (grid_2.material==grid_1.material).all() @pytest.mark.parametrize('periodic',[True,False]) @@ -52,9 +52,9 @@ class TestSeeds: size = np.ones(3) + np.random.random(3) coords = grid_filters.coordinates0_point(cells,size).reshape(-1,3) np.random.shuffle(coords) - grid_1 = Grid.from_Voronoi_tessellation(cells,size,coords) + grid_1 = GeomGrid.from_Voronoi_tessellation(cells,size,coords) coords,material = seeds.from_grid(grid_1,average=average,periodic=periodic) - grid_2 = Grid.from_Voronoi_tessellation(cells,size,coords,material) + grid_2 = GeomGrid.from_Voronoi_tessellation(cells,size,coords,material) assert (grid_2.material==grid_1.material).all() @pytest.mark.parametrize('periodic',[True,False]) @@ -65,7 +65,7 @@ class TestSeeds: N_seeds = np.random.randint(30,300) size = np.ones(3) + np.random.random(3) coords = seeds.from_random(size,N_seeds,cells) - grid = Grid.from_Voronoi_tessellation(cells,size,coords) + grid = GeomGrid.from_Voronoi_tessellation(cells,size,coords) selection=np.random.randint(N_seeds)+1 coords,material = seeds.from_grid(grid,average=average,periodic=periodic,invert_selection=invert,selection=[selection]) assert selection not in material if invert else (selection==material).all() From 8427adcaecce3a0a759ade9b81a68fc155f9480c Mon Sep 17 00:00:00 2001 From: Test User Date: Tue, 28 Nov 2023 01:40:16 +0100 Subject: [PATCH 72/84] [skip ci] updated version information after successful test of v3.0.0-alpha8-50-gf02c1dc44 --- VERSION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/VERSION b/VERSION index 9ad10d710..8a4f395e9 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -3.0.0-alpha8-47-gf86d4be18 +3.0.0-alpha8-50-gf02c1dc44 From 779a6e7a05e884db6f3fc64a4b5e9ff231fde972 Mon Sep 17 00:00:00 2001 From: Cathy Bing Date: Tue, 28 Nov 2023 00:15:18 -0500 Subject: [PATCH 73/84] edge dipole annihilation via climb is not disabled anymore when stress is zero --- PRIVATE | 2 +- ...phase_mechanical_plastic_dislotungsten.f90 | 9 +++- src/phase_mechanical_plastic_dislotwin.f90 | 42 +++++++++---------- 3 files changed, 28 insertions(+), 25 deletions(-) diff --git a/PRIVATE b/PRIVATE index 2cfe059d4..6f4a7a859 160000 --- a/PRIVATE +++ b/PRIVATE @@ -1 +1 @@ -Subproject commit 2cfe059d411b00e62e005107c3031ec988b2cd84 +Subproject commit 6f4a7a8598dfceae5c2178c8c2554245d8164cb2 diff --git a/src/phase_mechanical_plastic_dislotungsten.f90 b/src/phase_mechanical_plastic_dislotungsten.f90 index ad9853b3a..2c7429059 100644 --- a/src/phase_mechanical_plastic_dislotungsten.f90 +++ b/src/phase_mechanical_plastic_dislotungsten.f90 @@ -357,8 +357,8 @@ module function dislotungsten_dotState(Mp,ph,en) result(dotState) dot_gamma = abs(dot_gamma) where(dEq0(dot_gamma)) + d_hat = dst%Lambda_sl(:,en) ! upper limit dot_rho_dip_formation = 0.0_pREAL - dot_rho_dip_climb = 0.0_pREAL else where d_hat = math_clip(mu*prm%b_sl/(8.0_pREAL*PI*(1.0_pREAL-nu)*tau_eff), & left = prm%d_caron, & ! lower limit @@ -366,9 +366,14 @@ module function dislotungsten_dotState(Mp,ph,en) result(dotState) dot_rho_dip_formation = merge(dot_gamma * 2.0_pREAL*(d_hat-prm%d_caron)/prm%b_sl * stt%rho_mob(:,en), & 0.0_pREAL, & prm%dipoleformation) + end where + + where(dEq0(d_hat-prm%d_caron)) + dot_rho_dip_climb = 0.0_pREAL + else where v_cl = (3.0_pREAL*mu*prm%D_0*exp(-prm%Q_cl/(K_B*T))*prm%f_at/(2.0_pREAL*PI*K_B*T)) & * (1.0_pREAL/(d_hat+prm%d_caron)) - dot_rho_dip_climb = (4.0_pREAL*v_cl*stt%rho_dip(:,en))/(d_hat-prm%d_caron) ! ToDo: Discuss with Franz: Stress dependency? + dot_rho_dip_climb = (4.0_pREAL*v_cl*stt%rho_dip(:,en))/(d_hat-prm%d_caron) ! ToDo: Discuss with Franz: Stress dependency? end where dot_rho_mob = dot_gamma / (prm%b_sl*dst%Lambda_sl(:,en)) & ! multiplication diff --git a/src/phase_mechanical_plastic_dislotwin.f90 b/src/phase_mechanical_plastic_dislotwin.f90 index 0bd0e1355..aa6c83035 100644 --- a/src/phase_mechanical_plastic_dislotwin.f90 +++ b/src/phase_mechanical_plastic_dislotwin.f90 @@ -671,33 +671,31 @@ module function dislotwin_dotState(Mp,ph,en) result(dotState) tau = math_tensordot(Mp,prm%P_sl(1:3,1:3,i)) significantSlipStress: if (dEq0(tau) .or. prm%omitDipoles) then + d_hat = dst%Lambda_sl(i,en) dot_rho_dip_formation(i) = 0.0_pREAL - dot_rho_dip_climb(i) = 0.0_pREAL else significantSlipStress - d_hat = 3.0_pREAL*mu*prm%b_sl(i)/(16.0_pREAL*PI*abs(tau)) - d_hat = math_clip(d_hat, right = dst%Lambda_sl(i,en)) - d_hat = math_clip(d_hat, left = prm%d_caron(i)) - + d_hat = math_clip(3.0_pREAL*mu*prm%b_sl(i)/(16.0_pREAL*PI*abs(tau)), & + left = prm%d_caron(i), & + right = dst%Lambda_sl(i,en)) dot_rho_dip_formation(i) = 2.0_pREAL*(d_hat-prm%d_caron(i))/prm%b_sl(i) & * stt%rho_mob(i,en)*abs_dot_gamma_sl(i) - - if (dEq(d_hat,prm%d_caron(i))) then - dot_rho_dip_climb(i) = 0.0_pREAL - else - ! Argon & Moffat, Acta Metallurgica, Vol. 29, pg 293 to 299, 1981 - sigma_cl = dot_product(prm%n0_sl(1:3,i),matmul(Mp,prm%n0_sl(1:3,i))) - if (prm%extendedDislocations) then - b_d = 24.0_pREAL*PI*(1.0_pREAL - nu)/(2.0_pREAL + nu) * prm%Gamma_sf%at(T) / (mu*prm%b_sl(i)) - else - b_d = 1.0_pREAL - end if - v_cl = 2.0_pREAL*prm%omega*b_d**2*exp(-prm%Q_cl/(K_B*T)) & - * (exp(abs(sigma_cl)*prm%b_sl(i)**3/(K_B*T)) - 1.0_pREAL) - - dot_rho_dip_climb(i) = 4.0_pREAL*v_cl*stt%rho_dip(i,en) & - / (d_hat-prm%d_caron(i)) - end if end if significantSlipStress + + if (dEq(d_hat,prm%d_caron(i))) then + dot_rho_dip_climb(i) = 0.0_pREAL + else + ! Argon & Moffat, Acta Metallurgica, Vol. 29, pg 293 to 299, 1981 + sigma_cl = dot_product(prm%n0_sl(1:3,i),matmul(Mp,prm%n0_sl(1:3,i))) + if (prm%extendedDislocations) then + b_d = 24.0_pREAL*PI*(1.0_pREAL - nu)/(2.0_pREAL + nu) * prm%Gamma_sf%at(T) / (mu*prm%b_sl(i)) + else + b_d = 1.0_pREAL + end if + v_cl = 2.0_pREAL*prm%omega*b_d**2*exp(-prm%Q_cl/(K_B*T)) & + * (exp(abs(sigma_cl)*prm%b_sl(i)**3/(K_B*T)) - 1.0_pREAL) + dot_rho_dip_climb(i) = 4.0_pREAL*v_cl*stt%rho_dip(i,en) & + / (d_hat-prm%d_caron(i)) + end if end do slipState dot_rho_mob = abs_dot_gamma_sl/(prm%b_sl*dst%Lambda_sl(:,en)) & From b0ba1e78b77462848b5910131a7e0352a2ca06e0 Mon Sep 17 00:00:00 2001 From: Cathy Bing Date: Tue, 28 Nov 2023 01:09:20 -0500 Subject: [PATCH 74/84] two-step math_clip --- src/phase_mechanical_plastic_dislotungsten.f90 | 7 ++++--- src/phase_mechanical_plastic_dislotwin.f90 | 7 ++++--- 2 files changed, 8 insertions(+), 6 deletions(-) diff --git a/src/phase_mechanical_plastic_dislotungsten.f90 b/src/phase_mechanical_plastic_dislotungsten.f90 index 2c7429059..3efa0fa92 100644 --- a/src/phase_mechanical_plastic_dislotungsten.f90 +++ b/src/phase_mechanical_plastic_dislotungsten.f90 @@ -360,9 +360,10 @@ module function dislotungsten_dotState(Mp,ph,en) result(dotState) d_hat = dst%Lambda_sl(:,en) ! upper limit dot_rho_dip_formation = 0.0_pREAL else where - d_hat = math_clip(mu*prm%b_sl/(8.0_pREAL*PI*(1.0_pREAL-nu)*tau_eff), & - left = prm%d_caron, & ! lower limit - right = dst%Lambda_sl(:,en)) ! upper limit + d_hat = mu*prm%b_sl/(8.0_pREAL*PI*(1.0_pREAL-nu)*tau_eff) + d_hat = math_clip(d_hat, right = dst%Lambda_sl(:,en)) ! upper limit + d_hat = math_clip(d_hat, left = prm%d_caron) ! lower limit + dot_rho_dip_formation = merge(dot_gamma * 2.0_pREAL*(d_hat-prm%d_caron)/prm%b_sl * stt%rho_mob(:,en), & 0.0_pREAL, & prm%dipoleformation) diff --git a/src/phase_mechanical_plastic_dislotwin.f90 b/src/phase_mechanical_plastic_dislotwin.f90 index aa6c83035..515a495f0 100644 --- a/src/phase_mechanical_plastic_dislotwin.f90 +++ b/src/phase_mechanical_plastic_dislotwin.f90 @@ -674,9 +674,10 @@ module function dislotwin_dotState(Mp,ph,en) result(dotState) d_hat = dst%Lambda_sl(i,en) dot_rho_dip_formation(i) = 0.0_pREAL else significantSlipStress - d_hat = math_clip(3.0_pREAL*mu*prm%b_sl(i)/(16.0_pREAL*PI*abs(tau)), & - left = prm%d_caron(i), & - right = dst%Lambda_sl(i,en)) + d_hat = 3.0_pREAL*mu*prm%b_sl(i)/(16.0_pREAL*PI*abs(tau)) + d_hat = math_clip(d_hat, right = dst%Lambda_sl(i,en)) + d_hat = math_clip(d_hat, left = prm%d_caron(i)) + dot_rho_dip_formation(i) = 2.0_pREAL*(d_hat-prm%d_caron(i))/prm%b_sl(i) & * stt%rho_mob(i,en)*abs_dot_gamma_sl(i) end if significantSlipStress From 9710ec11f06b488d2953b6550308522f50c8e561 Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Tue, 28 Nov 2023 08:16:47 +0100 Subject: [PATCH 75/84] load data from SPPARKS https://spparks.github.io/ --- python/damask/_geomgrid.py | 68 ++++++++++++++++--- .../tests/resources/GeomGrid/SPPARKS_dump.vti | 19 ++++++ python/tests/test_GeomGrid.py | 6 ++ 3 files changed, 82 insertions(+), 11 deletions(-) create mode 100644 python/tests/resources/GeomGrid/SPPARKS_dump.vti diff --git a/python/damask/_geomgrid.py b/python/damask/_geomgrid.py index 717d42907..9384de15c 100644 --- a/python/damask/_geomgrid.py +++ b/python/damask/_geomgrid.py @@ -197,10 +197,41 @@ class GeomGrid: @staticmethod - def load(fname: Union[str, Path]) -> 'GeomGrid': + def _load(fname: Union[str, Path],label) -> 'GeomGrid': """ Load from VTK ImageData file. + Parameters + ---------- + fname : str or pathlib.Path + VTK ImageData file to read. + Valid extension is .vti, which will be appended if not given. + label : str + Label of the dataset containing the material IDs. + + Returns + ------- + loaded : damask.GeomGrid + GeomGrid-based geometry from file. + + """ + v = VTK.load(fname if str(fname).endswith('.vti') else str(fname)+'.vti') + cells = np.array(v.vtk_data.GetDimensions())-1 + bbox = np.array(v.vtk_data.GetBounds()).reshape(3,2).T + ic = {label:v.get(l).reshape(cells,order='F') for l in set(v.labels['Cell Data']) - {label}} + + return GeomGrid(material = v.get(label).reshape(cells,order='F'), + size = bbox[1] - bbox[0], + origin = bbox[0], + initial_conditions = ic, + comments = v.comments, + ) + + @staticmethod + def load(fname: Union[str, Path]) -> 'GeomGrid': + """ + Load from VTK ImageData file with material IDs stored as 'material'. + Parameters ---------- fname : str or pathlib.Path @@ -213,17 +244,32 @@ class GeomGrid: GeomGrid-based geometry from file. """ - v = VTK.load(fname if str(fname).endswith('.vti') else str(fname)+'.vti') - cells = np.array(v.vtk_data.GetDimensions())-1 - bbox = np.array(v.vtk_data.GetBounds()).reshape(3,2).T - ic = {label:v.get(label).reshape(cells,order='F') for label in set(v.labels['Cell Data']) - {'material'}} + return GeomGrid._load(fname,'material') - return GeomGrid(material = v.get('material').reshape(cells,order='F'), - size = bbox[1] - bbox[0], - origin = bbox[0], - initial_conditions = ic, - comments = v.comments, - ) + + @staticmethod + def load_SPPARKS(fname: Union[str, Path]) -> 'GeomGrid': + """ + Load from SPPARKs VTK dump. + + Parameters + ---------- + fname : str or pathlib.Path + SPPARKS VTK dump file to read. + Valid extension is .vti, which will be appended if not given. + + Returns + ------- + loaded : damask.GeomGrid + GeomGrid-based geometry from file. + + Notes + ----- + A SPPARKs VTI dump is equivalent to a DAMASK VTI file + where 'material' is renamed to 'spins'. + + """ + return GeomGrid._load(fname,'spins') @typing.no_type_check diff --git a/python/tests/resources/GeomGrid/SPPARKS_dump.vti b/python/tests/resources/GeomGrid/SPPARKS_dump.vti new file mode 100644 index 000000000..a0ac422df --- /dev/null +++ b/python/tests/resources/GeomGrid/SPPARKS_dump.vti @@ -0,0 +1,19 @@ + + + + + + AQAAAACAAAABAAAACQAAAA==eF5jAAAAAQAB + + + + + + + + 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 + + + + + diff --git a/python/tests/test_GeomGrid.py b/python/tests/test_GeomGrid.py index f09ba07bf..76f30701f 100644 --- a/python/tests/test_GeomGrid.py +++ b/python/tests/test_GeomGrid.py @@ -92,6 +92,12 @@ class TestGeomGrid: default.material -= 1 assert GeomGrid.load_ASCII(tmp_path/'ASCII') == default + def test_save_load_SPPARKS(self,res_path,tmp_path): + v = VTK.load(res_path/'SPPARKS_dump.vti') + v.set('material',v.get('spins')).save(tmp_path/'SPPARKS_dump.vti',parallel=False) + assert np.all(GeomGrid.load_SPPARKS(res_path/'SPPARKS_dump.vti').material == \ + GeomGrid.load(tmp_path/'SPPARKS_dump.vti').material) + def test_invalid_origin(self,default): with pytest.raises(ValueError): GeomGrid(default.material[1:,1:,1:], From 9aa68e83a20702f80d141f0be2205ad5dcc102e2 Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Tue, 28 Nov 2023 11:50:12 +0100 Subject: [PATCH 76/84] new delete function for VTK follows 'damask.Table' and makes GeomGrid.load_SPPARKS easier --- python/damask/_vtk.py | 39 +++++++++++++++++++++++++++++++++-- python/tests/test_GeomGrid.py | 6 +++--- python/tests/test_VTK.py | 7 +++++++ 3 files changed, 47 insertions(+), 5 deletions(-) diff --git a/python/damask/_vtk.py b/python/damask/_vtk.py index f8a58d73b..920e27781 100644 --- a/python/damask/_vtk.py +++ b/python/damask/_vtk.py @@ -457,7 +457,7 @@ class VTK: data: Union[None, np.ndarray, np.ma.MaskedArray] = None, info: Optional[str] = None, *, - table: Optional['Table'] = None): + table: Optional['Table'] = None) -> 'VTK': """ Add new or replace existing point or cell data. @@ -534,7 +534,6 @@ class VTK: else: raise TypeError - return dup @@ -581,6 +580,42 @@ class VTK: raise KeyError(f'array "{label}" not found') + def delete(self, + label: str) -> 'VTK': + """ + Delete either cell or point data. + + Cell data takes precedence over point data, i.e. this + function assumes that labels are unique among cell and + point data. + + Parameters + ---------- + label : str + Data label. + + Returns + ------- + updated : damask.VTK + Updated VTK-based geometry. + + """ + dup = self.copy() + cell_data = dup.vtk_data.GetCellData() + for a in range(cell_data.GetNumberOfArrays()): + if cell_data.GetArrayName(a) == label: + dup.vtk_data.GetCellData().RemoveArray(label) + return dup + + point_data = self.vtk_data.GetPointData() + for a in range(point_data.GetNumberOfArrays()): + if point_data.GetArrayName(a) == label: + dup.vtk_data.GetPointData().RemoveArray(label) + return dup + + raise KeyError(f'array "{label}" not found') + + def show(self, label: Optional[str] = None, colormap: Union[Colormap, str] = 'cividis'): diff --git a/python/tests/test_GeomGrid.py b/python/tests/test_GeomGrid.py index 76f30701f..b91c73e5c 100644 --- a/python/tests/test_GeomGrid.py +++ b/python/tests/test_GeomGrid.py @@ -94,9 +94,9 @@ class TestGeomGrid: def test_save_load_SPPARKS(self,res_path,tmp_path): v = VTK.load(res_path/'SPPARKS_dump.vti') - v.set('material',v.get('spins')).save(tmp_path/'SPPARKS_dump.vti',parallel=False) - assert np.all(GeomGrid.load_SPPARKS(res_path/'SPPARKS_dump.vti').material == \ - GeomGrid.load(tmp_path/'SPPARKS_dump.vti').material) + v.set('material',v.get('spins')).delete('spins').save(tmp_path/'SPPARKS_dump.vti',parallel=False) + assert GeomGrid.load_SPPARKS(res_path/'SPPARKS_dump.vti') == \ + GeomGrid.load(tmp_path/'SPPARKS_dump.vti') def test_invalid_origin(self,default): with pytest.raises(ValueError): diff --git a/python/tests/test_VTK.py b/python/tests/test_VTK.py index 0368d1d16..e3cb64ca5 100644 --- a/python/tests/test_VTK.py +++ b/python/tests/test_VTK.py @@ -199,6 +199,13 @@ class TestVTK: mask_manual = default.set('D',np.where(masked.mask,masked.fill_value,masked)) assert mask_manual == mask_auto + @pytest.mark.parametrize('mode',['cells','points']) + def test_delete(self,default,mode): + data = np.random.rand(default.N_cells if mode == 'cells' else default.N_points).astype(np.float32) + v = default.set('D',data) + assert np.all(data == v.get('D')) + v = v.delete('D') + assert v == default @pytest.mark.parametrize('data_type,shape',[(float,(3,)), (float,(3,3)), From 33b1d7902d6467d5754f48e7e012e87986c135ea Mon Sep 17 00:00:00 2001 From: Test User Date: Tue, 28 Nov 2023 12:31:38 +0100 Subject: [PATCH 77/84] [skip ci] updated version information after successful test of v3.0.0-alpha8-53-g05c4fcbea --- VERSION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/VERSION b/VERSION index 8a4f395e9..f059ac0e2 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -3.0.0-alpha8-50-gf02c1dc44 +3.0.0-alpha8-53-g05c4fcbea From 59765ab0df7e81812da565168192bfd3f5d5d7d0 Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Tue, 28 Nov 2023 14:35:30 +0100 Subject: [PATCH 78/84] typo: do not overwrite 'label' --- python/damask/_geomgrid.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/python/damask/_geomgrid.py b/python/damask/_geomgrid.py index 9384de15c..bd6998822 100644 --- a/python/damask/_geomgrid.py +++ b/python/damask/_geomgrid.py @@ -197,7 +197,7 @@ class GeomGrid: @staticmethod - def _load(fname: Union[str, Path],label) -> 'GeomGrid': + def _load(fname: Union[str, Path], label: str) -> 'GeomGrid': """ Load from VTK ImageData file. @@ -218,7 +218,7 @@ class GeomGrid: v = VTK.load(fname if str(fname).endswith('.vti') else str(fname)+'.vti') cells = np.array(v.vtk_data.GetDimensions())-1 bbox = np.array(v.vtk_data.GetBounds()).reshape(3,2).T - ic = {label:v.get(l).reshape(cells,order='F') for l in set(v.labels['Cell Data']) - {label}} + ic = {l:v.get(l).reshape(cells,order='F') for l in set(v.labels['Cell Data']) - {label}} return GeomGrid(material = v.get(label).reshape(cells,order='F'), size = bbox[1] - bbox[0], From 7e56abf41cd6f6a2c960c7536c541ba3970619e9 Mon Sep 17 00:00:00 2001 From: Philip Eisenlohr Date: Tue, 28 Nov 2023 17:21:06 +0000 Subject: [PATCH 79/84] language polish --- python/damask/_geomgrid.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/python/damask/_geomgrid.py b/python/damask/_geomgrid.py index bd6998822..0875197c3 100644 --- a/python/damask/_geomgrid.py +++ b/python/damask/_geomgrid.py @@ -250,7 +250,7 @@ class GeomGrid: @staticmethod def load_SPPARKS(fname: Union[str, Path]) -> 'GeomGrid': """ - Load from SPPARKs VTK dump. + Load from SPPARKS VTK dump. Parameters ---------- @@ -265,8 +265,8 @@ class GeomGrid: Notes ----- - A SPPARKs VTI dump is equivalent to a DAMASK VTI file - where 'material' is renamed to 'spins'. + A SPPARKS VTI dump is equivalent to a DAMASK VTI file, + but stores the materialID information as 'spins' rather than 'material'. """ return GeomGrid._load(fname,'spins') From 118247111b75a3ac0f88e6eacc7f7bc428e629fc Mon Sep 17 00:00:00 2001 From: Philip Eisenlohr Date: Tue, 28 Nov 2023 12:31:47 -0500 Subject: [PATCH 80/84] slightly shorter code --- python/damask/_vtk.py | 15 +++++++-------- python/tests/test_VTK.py | 5 ++--- 2 files changed, 9 insertions(+), 11 deletions(-) diff --git a/python/damask/_vtk.py b/python/damask/_vtk.py index 920e27781..08d8a1819 100644 --- a/python/damask/_vtk.py +++ b/python/damask/_vtk.py @@ -601,17 +601,16 @@ class VTK: """ dup = self.copy() + cell_data = dup.vtk_data.GetCellData() - for a in range(cell_data.GetNumberOfArrays()): - if cell_data.GetArrayName(a) == label: - dup.vtk_data.GetCellData().RemoveArray(label) - return dup + if label in [cell_data.GetArrayName(a) for a in range(cell_data.GetNumberOfArrays())]: + dup.vtk_data.GetCellData().RemoveArray(label) + return dup point_data = self.vtk_data.GetPointData() - for a in range(point_data.GetNumberOfArrays()): - if point_data.GetArrayName(a) == label: - dup.vtk_data.GetPointData().RemoveArray(label) - return dup + if label in [point_data.GetArrayName(a) for a in range(point_data.GetNumberOfArrays())]: + dup.vtk_data.GetPointData().RemoveArray(label) + return dup raise KeyError(f'array "{label}" not found') diff --git a/python/tests/test_VTK.py b/python/tests/test_VTK.py index e3cb64ca5..1f8237bc6 100644 --- a/python/tests/test_VTK.py +++ b/python/tests/test_VTK.py @@ -203,9 +203,8 @@ class TestVTK: def test_delete(self,default,mode): data = np.random.rand(default.N_cells if mode == 'cells' else default.N_points).astype(np.float32) v = default.set('D',data) - assert np.all(data == v.get('D')) - v = v.delete('D') - assert v == default + assert (v.get('D') == data).all() + assert v.delete('D') == default @pytest.mark.parametrize('data_type,shape',[(float,(3,)), (float,(3,3)), From 38c7e6a8e40bca3fafc3be8f16609c617b9f59f0 Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Tue, 28 Nov 2023 20:52:49 +0100 Subject: [PATCH 81/84] consistent code --- python/damask/_vtk.py | 22 ++++++++++------------ 1 file changed, 10 insertions(+), 12 deletions(-) diff --git a/python/damask/_vtk.py b/python/damask/_vtk.py index 08d8a1819..aa1e6fcbf 100644 --- a/python/damask/_vtk.py +++ b/python/damask/_vtk.py @@ -558,20 +558,18 @@ class VTK: """ cell_data = self.vtk_data.GetCellData() - for a in range(cell_data.GetNumberOfArrays()): - if cell_data.GetArrayName(a) == label: - try: - return vtk_to_numpy(cell_data.GetArray(a)) - except AttributeError: - vtk_array = cell_data.GetAbstractArray(a) # string array + if label in [cell_data.GetArrayName(a) for a in range(cell_data.GetNumberOfArrays())]: + try: + return vtk_to_numpy(cell_data.GetArray(label)) + except AttributeError: + vtk_array = cell_data.GetAbstractArray(label) # string array point_data = self.vtk_data.GetPointData() - for a in range(point_data.GetNumberOfArrays()): - if point_data.GetArrayName(a) == label: - try: - return vtk_to_numpy(point_data.GetArray(a)) - except AttributeError: - vtk_array = point_data.GetAbstractArray(a) # string array + if label in [point_data.GetArrayName(a) for a in range(point_data.GetNumberOfArrays())]: + try: + return vtk_to_numpy(point_data.GetArray(label)) + except AttributeError: + vtk_array = point_data.GetAbstractArray(label) # string array try: # string array From 89ba785938e6143aad348d54c75da6b59172134b Mon Sep 17 00:00:00 2001 From: Martin Diehl Date: Tue, 28 Nov 2023 21:08:53 +0100 Subject: [PATCH 82/84] using correct name (confirmed by user with original request) --- python/damask/_geomgrid.py | 4 ++-- python/tests/resources/GeomGrid/SPPARKS_dump.vti | 2 +- python/tests/test_GeomGrid.py | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/python/damask/_geomgrid.py b/python/damask/_geomgrid.py index 0875197c3..6690339fb 100644 --- a/python/damask/_geomgrid.py +++ b/python/damask/_geomgrid.py @@ -266,10 +266,10 @@ class GeomGrid: Notes ----- A SPPARKS VTI dump is equivalent to a DAMASK VTI file, - but stores the materialID information as 'spins' rather than 'material'. + but stores the materialID information as 'Spin' rather than 'material'. """ - return GeomGrid._load(fname,'spins') + return GeomGrid._load(fname,'Spin') @typing.no_type_check diff --git a/python/tests/resources/GeomGrid/SPPARKS_dump.vti b/python/tests/resources/GeomGrid/SPPARKS_dump.vti index a0ac422df..ca5bbf155 100644 --- a/python/tests/resources/GeomGrid/SPPARKS_dump.vti +++ b/python/tests/resources/GeomGrid/SPPARKS_dump.vti @@ -10,7 +10,7 @@ - + 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diff --git a/python/tests/test_GeomGrid.py b/python/tests/test_GeomGrid.py index b91c73e5c..ef8b3c10a 100644 --- a/python/tests/test_GeomGrid.py +++ b/python/tests/test_GeomGrid.py @@ -94,7 +94,7 @@ class TestGeomGrid: def test_save_load_SPPARKS(self,res_path,tmp_path): v = VTK.load(res_path/'SPPARKS_dump.vti') - v.set('material',v.get('spins')).delete('spins').save(tmp_path/'SPPARKS_dump.vti',parallel=False) + v.set('material',v.get('Spin')).delete('Spin').save(tmp_path/'SPPARKS_dump.vti',parallel=False) assert GeomGrid.load_SPPARKS(res_path/'SPPARKS_dump.vti') == \ GeomGrid.load(tmp_path/'SPPARKS_dump.vti') From 59793168be769b449d13a0fce1329a6b5b0536aa Mon Sep 17 00:00:00 2001 From: Test User Date: Wed, 29 Nov 2023 03:35:55 +0100 Subject: [PATCH 83/84] [skip ci] updated version information after successful test of v3.0.0-alpha8-58-gd8900558c --- VERSION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/VERSION b/VERSION index f059ac0e2..1e3cd4a53 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -3.0.0-alpha8-53-g05c4fcbea +3.0.0-alpha8-58-gd8900558c From ad601935027ffe22a2ed06ccc8df24f1f36470e1 Mon Sep 17 00:00:00 2001 From: Test User Date: Thu, 30 Nov 2023 09:28:19 +0100 Subject: [PATCH 84/84] [skip ci] updated version information after successful test of v3.0.0-alpha8-67-g9f8ecd44b --- VERSION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/VERSION b/VERSION index 1e3cd4a53..f48ee501b 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -3.0.0-alpha8-58-gd8900558c +3.0.0-alpha8-67-g9f8ecd44b

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