Merge remote-tracking branch 'origin/development' into petsc-64bit-integer
This commit is contained in:
commit
4bfc814a53
|
@ -45,7 +45,7 @@ variables:
|
|||
MPI_INTEL: "MPI/Intel/2022.0.1/IntelMPI/2021.5.0"
|
||||
# ++++++++++++ PETSc ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
|
||||
PETSC_GNU: "Libraries/PETSc/3.16.1/GNU-10-OpenMPI-4.1.1"
|
||||
PETSC_INTELLLVM: "Libraries/PETSc/3.16.2/oneAPI-2022.0.1-IntelMPI-2021.5.0"
|
||||
PETSC_INTELLLVM: "Libraries/PETSc/3.16.3/oneAPI-2022.0.1-IntelMPI-2021.5.0"
|
||||
PETSC_INTEL: "Libraries/PETSc/3.16.2/Intel-2022.0.1-IntelMPI-2021.5.0"
|
||||
# ++++++++++++ MSC Marc +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
|
||||
MSC: "FEM/MSC/2021.3.1"
|
||||
|
|
|
@ -88,16 +88,12 @@ else()
|
|||
message(FATAL_ERROR "Compiler type(CMAKE_Fortran_COMPILER_ID) not recognized")
|
||||
endif()
|
||||
|
||||
file(STRINGS "$ENV{PETSC_DIR}/$ENV{PETSC_ARCH}/lib/petsc/conf/petscvariables" PETSC_EXTERNAL_LIB REGEX "PETSC_WITH_EXTERNAL_LIB = .*$?")
|
||||
string(REGEX MATCHALL "-[lLW]([^\" ]+)" PETSC_EXTERNAL_LIB "${PETSC_EXTERNAL_LIB}")
|
||||
list(REMOVE_DUPLICATES PETSC_EXTERNAL_LIB)
|
||||
string(REPLACE ";" " " PETSC_EXTERNAL_LIB "${PETSC_EXTERNAL_LIB}")
|
||||
file(STRINGS "$ENV{PETSC_DIR}/$ENV{PETSC_ARCH}/lib/petsc/conf/petscvariables" PETSC_EXTERNAL_LIB REGEX "PETSC_EXTERNAL_LIB_BASIC = .*$?")
|
||||
string(REPLACE "PETSC_EXTERNAL_LIB_BASIC = " "" PETSC_EXTERNAL_LIB "${PETSC_EXTERNAL_LIB}")
|
||||
message("PETSC_EXTERNAL_LIB:\n${PETSC_EXTERNAL_LIB}\n")
|
||||
|
||||
file(STRINGS "$ENV{PETSC_DIR}/$ENV{PETSC_ARCH}/lib/petsc/conf/petscvariables" PETSC_INCLUDES REGEX "PETSC_FC_INCLUDES = .*$?")
|
||||
string(REGEX MATCHALL "-I([^\" ]+)" PETSC_INCLUDES "${PETSC_INCLUDES}")
|
||||
list(REMOVE_DUPLICATES PETSC_INCLUDES)
|
||||
string(REPLACE ";" " " PETSC_INCLUDES "${PETSC_INCLUDES}")
|
||||
string(REPLACE "PETSC_FC_INCLUDES = " "" PETSC_INCLUDES "${PETSC_INCLUDES}")
|
||||
message("PETSC_INCLUDES:\n${PETSC_INCLUDES}\n")
|
||||
|
||||
set(CMAKE_Fortran_FLAGS_${CMAKE_BUILD_TYPE} "${BUILDCMD_PRE} ${OPENMP_FLAGS} ${STANDARD_CHECK} ${OPTIMIZATION_FLAGS} ${COMPILE_FLAGS} ${PRECISION_FLAGS}")
|
||||
|
@ -109,7 +105,7 @@ if(CMAKE_BUILD_TYPE STREQUAL "DEBUG")
|
|||
endif()
|
||||
|
||||
set(CMAKE_Fortran_FLAGS_${CMAKE_BUILD_TYPE} "${CMAKE_Fortran_FLAGS_${CMAKE_BUILD_TYPE}} ${PETSC_INCLUDES} ${BUILDCMD_POST}")
|
||||
set(CMAKE_Fortran_LINK_EXECUTABLE "${CMAKE_Fortran_LINK_EXECUTABLE} <OBJECTS> -o <TARGET> <LINK_LIBRARIES> ${PETSC_EXTERNAL_LIB} -lz ${BUILDCMD_POST}")
|
||||
set(CMAKE_Fortran_LINK_EXECUTABLE "${CMAKE_Fortran_LINK_EXECUTABLE} <OBJECTS> -o <TARGET> <LINK_LIBRARIES> -L${PETSC_LIBRARY_DIRS} -lpetsc ${PETSC_EXTERNAL_LIB} -lz ${BUILDCMD_POST}")
|
||||
|
||||
message("Fortran Compiler Flags:\n${CMAKE_Fortran_FLAGS_${CMAKE_BUILD_TYPE}}\n")
|
||||
message("C Compiler Flags:\n${CMAKE_C_FLAGS_${CMAKE_BUILD_TYPE}}\n")
|
||||
|
|
6
Makefile
6
Makefile
|
@ -10,14 +10,12 @@ all: grid mesh
|
|||
.PHONY: grid
|
||||
grid:
|
||||
@cmake -B build/grid -DDAMASK_SOLVER=grid -DCMAKE_INSTALL_PREFIX=${PWD} -DCMAKE_BUILD_TYPE=${BUILD_TYPE} -DBUILDCMD_POST=${BUILDCMD_POST} -DBUILDCMD_PRE=${BUILDCMD_PRE} -DOPTIMIZATION=${OPTIMIZATION} -DOPENMP=${OPENMP}
|
||||
@cmake --build build/grid --parallel
|
||||
@cmake --install build/grid
|
||||
@cmake --build build/grid --parallel --target install
|
||||
|
||||
.PHONY: mesh
|
||||
mesh:
|
||||
@cmake -B build/mesh -DDAMASK_SOLVER=mesh -DCMAKE_INSTALL_PREFIX=${PWD} -DCMAKE_BUILD_TYPE=${BUILD_TYPE} -DBUILDCMD_POST=${BUILDCMD_POST} -DBUILDCMD_PRE=${BUILDCMD_PRE} -DOPTIMIZATION=${OPTIMIZATION} -DOPENMP=${OPENMP}
|
||||
@cmake --build build/mesh --parallel
|
||||
@cmake --install build/mesh
|
||||
@cmake --build build/mesh --parallel --target install
|
||||
|
||||
.PHONY: clean
|
||||
clean:
|
||||
|
|
2
PRIVATE
2
PRIVATE
|
@ -1 +1 @@
|
|||
Subproject commit 96c32ba4237a51eaad92cd139e1a716ee5b32493
|
||||
Subproject commit b898a8b5552bd9d1c555edc3d8134564dd32fe53
|
|
@ -1,11 +1,6 @@
|
|||
# Tasan et.al. 2015 Acta Materalia
|
||||
# Tasan et.al. 2015 International Journal of Plasticity
|
||||
# Diehl et.al. 2015 Meccanica
|
||||
Martensite:
|
||||
lattice: cI
|
||||
mechanical:
|
||||
elastic: {C_11: 417.4e+9, C_12: 242.4e+9, C_44: 211.1e+9, type: Hooke}
|
||||
plastic:
|
||||
N_sl: [12, 12]
|
||||
a_sl: 2.0
|
||||
dot_gamma_0_sl: 0.001
|
||||
|
|
|
@ -0,0 +1,6 @@
|
|||
references:
|
||||
- H.M. Ledbetter
|
||||
physica status solidi (a) 85(1):89-96, 1984
|
||||
https://doi.org/10.1002/pssa.2210850111
|
||||
lattice: cF
|
||||
rho: 7937.0
|
|
@ -0,0 +1,7 @@
|
|||
type: thermalexpansion
|
||||
references:
|
||||
- R.H. Bogaard et al.
|
||||
Thermochimica Acta 218:373-393, 1993
|
||||
https://doi.org/10.1016/0040-6031(93)80437-F
|
||||
A_11: 15.0e-6
|
||||
T_ref: 300.0
|
|
@ -0,0 +1,8 @@
|
|||
type: Hooke
|
||||
references:
|
||||
- H.M. Ledbetter
|
||||
physica status solidi (a) 85(1):89-96, 1984
|
||||
https://doi.org/10.1002/pssa.2210850111
|
||||
C_11: 204.6e+9
|
||||
C_12: 137.7e+9
|
||||
C_44: 126.2e+9
|
|
@ -0,0 +1,8 @@
|
|||
type: Hooke
|
||||
references:
|
||||
- S.A. Kim and W.L. Johnson,
|
||||
Materials Science & Engineering A 452-453:633-639, 2007,
|
||||
https://doi.org/10.1016/j.msea.2006.11.147
|
||||
C_11: 268.1e+9
|
||||
C_12: 111.2e+9
|
||||
C_44: 79.06e+9
|
|
@ -4,7 +4,8 @@ references:
|
|||
International Journal of Plasticity 134:102779, 2020,
|
||||
https://doi.org/10.1016/j.ijplas.2020.102779
|
||||
- K. Sedighiani et al.,
|
||||
Mechanics of Materials, submitted
|
||||
Mechanics of Materials, 164:104117, 2022,
|
||||
https://doi.org/10.1016/j.mechmat.2021.104117
|
||||
output: [rho_dip, rho_mob]
|
||||
N_sl: [12, 12]
|
||||
b_sl: [2.49e-10, 2.49e-10]
|
||||
|
|
|
@ -0,0 +1,9 @@
|
|||
references:
|
||||
- B.F. Blackwell et al.
|
||||
Proceedings of 34th National Heat Transfer Conference 2000
|
||||
https://www.osti.gov/servlets/purl/760791
|
||||
- R.H. Bogaard et al.
|
||||
Thermochimica Acta 218:373-393, 1993
|
||||
https://doi.org/10.1016/0040-6031(93)80437-F
|
||||
C_p: 470.0
|
||||
K_11: 14.34
|
|
@ -67,9 +67,7 @@ os.system(f'xvfb-run -a {executable} -compile {menu_file}')
|
|||
|
||||
print('setting file access rights...')
|
||||
|
||||
files = (glob.glob(str(marc_root/f'marc{marc_version}/tools/*_damask*')) +
|
||||
for file in (glob.glob(str(marc_root/f'marc{marc_version}/tools/*_damask*')) +
|
||||
glob.glob(str(marc_root/f'mentat{marc_version}/bin/kill[4-6]')) +
|
||||
glob.glob(str(marc_root/f'mentat{marc_version}/bin/submit[4-6]')))
|
||||
|
||||
for file in files:
|
||||
glob.glob(str(marc_root/f'mentat{marc_version}/bin/submit[4-6]'))):
|
||||
os.chmod(file , 0o755)
|
||||
|
|
|
@ -1,71 +0,0 @@
|
|||
#!/usr/bin/env python3
|
||||
|
||||
import os
|
||||
import sys
|
||||
from io import StringIO
|
||||
from optparse import OptionParser
|
||||
|
||||
import damask
|
||||
|
||||
|
||||
scriptName = os.path.splitext(os.path.basename(__file__))[0]
|
||||
scriptID = ' '.join([scriptName,damask.version])
|
||||
|
||||
|
||||
# --------------------------------------------------------------------
|
||||
# MAIN
|
||||
# --------------------------------------------------------------------
|
||||
|
||||
parser = OptionParser(usage='%prog options [ASCIItable(s)]', description = """
|
||||
Add displacments resulting from deformation gradient field.
|
||||
Operates on periodic three-dimensional x,y,z-ordered data sets.
|
||||
Outputs at cell centers or cell nodes (into separate file).
|
||||
|
||||
""", version = scriptID)
|
||||
|
||||
parser.add_option('-f',
|
||||
'--defgrad',
|
||||
dest = 'f',
|
||||
metavar = 'string',
|
||||
help = 'label of deformation gradient [%default]')
|
||||
parser.add_option('-p',
|
||||
'--pos', '--position',
|
||||
dest = 'pos',
|
||||
metavar = 'string',
|
||||
help = 'label of coordinates [%default]')
|
||||
parser.add_option('--nodal',
|
||||
dest = 'nodal',
|
||||
action = 'store_true',
|
||||
help = 'output nodal (instead of cell-centered) displacements')
|
||||
|
||||
parser.set_defaults(f = 'f',
|
||||
pos = 'pos',
|
||||
)
|
||||
|
||||
(options,filenames) = parser.parse_args()
|
||||
|
||||
for name in filenames:
|
||||
damask.util.report(scriptName,name)
|
||||
|
||||
table = damask.Table.load(StringIO(''.join(sys.stdin.read())) if name is None else name)
|
||||
grid,size,origin = damask.grid_filters.cellsSizeOrigin_coordinates0_point(table.get(options.pos))
|
||||
|
||||
F = table.get(options.f).reshape(tuple(grid)+(-1,),order='F').reshape(tuple(grid)+(3,3))
|
||||
if options.nodal:
|
||||
damask.Table(damask.grid_filters.coordinates0_node(grid,size).reshape(-1,3,order='F'),
|
||||
{'pos':(3,)})\
|
||||
.add('avg({}).{}'.format(options.f,options.pos),
|
||||
damask.grid_filters.displacement_avg_node(size,F).reshape(-1,3,order='F'),
|
||||
scriptID+' '+' '.join(sys.argv[1:]))\
|
||||
.add('fluct({}).{}'.format(options.f,options.pos),
|
||||
damask.grid_filters.displacement_fluct_node(size,F).reshape(-1,3,order='F'),
|
||||
scriptID+' '+' '.join(sys.argv[1:]))\
|
||||
.save((sys.stdout if name is None else os.path.splitext(name)[0]+'_nodal.txt'))
|
||||
else:
|
||||
table.add('avg({}).{}'.format(options.f,options.pos),
|
||||
damask.grid_filters.displacement_avg_point(size,F).reshape(-1,3,order='F'),
|
||||
scriptID+' '+' '.join(sys.argv[1:]))\
|
||||
.add('fluct({}).{}'.format(options.f,options.pos),
|
||||
damask.grid_filters.displacement_fluct_point(size,F).reshape(-1,3,order='F'),
|
||||
scriptID+' '+' '.join(sys.argv[1:]))\
|
||||
.save((sys.stdout if name is None else name))
|
|
@ -1 +1 @@
|
|||
v3.0.0-alpha5-333-g01cd92755
|
||||
v3.0.0-alpha5-375-g76fe2d2b3
|
||||
|
|
|
@ -8,6 +8,7 @@ with open(_Path(__file__).parent/_Path('VERSION')) as _f:
|
|||
version = _re.sub(r'^v','',_f.readline().strip())
|
||||
__version__ = version
|
||||
|
||||
from . import _typehints # noqa
|
||||
from . import util # noqa
|
||||
from . import seeds # noqa
|
||||
from . import tensor # noqa
|
||||
|
|
|
@ -3,13 +3,9 @@ import json
|
|||
import functools
|
||||
import colorsys
|
||||
from pathlib import Path
|
||||
from typing import Sequence, Union, TextIO
|
||||
from typing import Union, TextIO
|
||||
|
||||
import numpy as np
|
||||
try:
|
||||
from numpy.typing import ArrayLike
|
||||
except ImportError:
|
||||
ArrayLike = Union[np.ndarray,Sequence[float]] # type: ignore
|
||||
import scipy.interpolate as interp
|
||||
import matplotlib as mpl
|
||||
if os.name == 'posix' and 'DISPLAY' not in os.environ:
|
||||
|
@ -18,6 +14,7 @@ import matplotlib.pyplot as plt
|
|||
from matplotlib import cm
|
||||
from PIL import Image
|
||||
|
||||
from ._typehints import FloatSequence, FileHandle
|
||||
from . import util
|
||||
from . import Table
|
||||
|
||||
|
@ -82,8 +79,8 @@ class Colormap(mpl.colors.ListedColormap):
|
|||
|
||||
|
||||
@staticmethod
|
||||
def from_range(low: ArrayLike,
|
||||
high: ArrayLike,
|
||||
def from_range(low: FloatSequence,
|
||||
high: FloatSequence,
|
||||
name: str = 'DAMASK colormap',
|
||||
N: int = 256,
|
||||
model: str = 'rgb') -> 'Colormap':
|
||||
|
@ -197,7 +194,7 @@ class Colormap(mpl.colors.ListedColormap):
|
|||
|
||||
|
||||
def at(self,
|
||||
fraction : Union[float,Sequence[float]]) -> np.ndarray:
|
||||
fraction : Union[float,FloatSequence]) -> np.ndarray:
|
||||
"""
|
||||
Interpolate color at fraction.
|
||||
|
||||
|
@ -229,14 +226,14 @@ class Colormap(mpl.colors.ListedColormap):
|
|||
|
||||
def shade(self,
|
||||
field: np.ndarray,
|
||||
bounds: ArrayLike = None,
|
||||
bounds: FloatSequence = None,
|
||||
gap: float = None) -> Image:
|
||||
"""
|
||||
Generate PIL image of 2D field using colormap.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
field : numpy.array, shape (:,:)
|
||||
field : numpy.ndarray, shape (:,:)
|
||||
Data to be shaded.
|
||||
bounds : sequence of float, len (2), optional
|
||||
Value range (left,right) spanned by colormap.
|
||||
|
@ -296,7 +293,7 @@ class Colormap(mpl.colors.ListedColormap):
|
|||
|
||||
|
||||
def _get_file_handle(self,
|
||||
fname: Union[TextIO, str, Path, None],
|
||||
fname: Union[FileHandle, None],
|
||||
suffix: str = '') -> TextIO:
|
||||
"""
|
||||
Provide file handle.
|
||||
|
@ -323,7 +320,7 @@ class Colormap(mpl.colors.ListedColormap):
|
|||
return fname
|
||||
|
||||
|
||||
def save_paraview(self, fname: Union[TextIO, str, Path] = None):
|
||||
def save_paraview(self, fname: FileHandle = None):
|
||||
"""
|
||||
Save as JSON file for use in Paraview.
|
||||
|
||||
|
@ -350,7 +347,7 @@ class Colormap(mpl.colors.ListedColormap):
|
|||
fhandle.write('\n')
|
||||
|
||||
|
||||
def save_ASCII(self, fname: Union[TextIO, str, Path] = None):
|
||||
def save_ASCII(self, fname: FileHandle = None):
|
||||
"""
|
||||
Save as ASCII file.
|
||||
|
||||
|
@ -365,7 +362,7 @@ class Colormap(mpl.colors.ListedColormap):
|
|||
t.save(self._get_file_handle(fname,'.txt'))
|
||||
|
||||
|
||||
def save_GOM(self, fname: Union[TextIO, str, Path] = None):
|
||||
def save_GOM(self, fname: FileHandle = None):
|
||||
"""
|
||||
Save as ASCII file for use in GOM Aramis.
|
||||
|
||||
|
@ -385,7 +382,7 @@ class Colormap(mpl.colors.ListedColormap):
|
|||
self._get_file_handle(fname,'.legend').write(GOM_str)
|
||||
|
||||
|
||||
def save_gmsh(self, fname: Union[TextIO, str, Path] = None):
|
||||
def save_gmsh(self, fname: FileHandle = None):
|
||||
"""
|
||||
Save as ASCII file for use in gmsh.
|
||||
|
||||
|
@ -616,7 +613,7 @@ class Colormap(mpl.colors.ListedColormap):
|
|||
|
||||
|
||||
@staticmethod
|
||||
def _lab2xyz(lab: np.ndarray, ref_white: np.ndarray = None) -> np.ndarray:
|
||||
def _lab2xyz(lab: np.ndarray, ref_white: np.ndarray = _REF_WHITE) -> np.ndarray:
|
||||
"""
|
||||
CIE Lab to CIE Xyz.
|
||||
|
||||
|
@ -624,6 +621,8 @@ class Colormap(mpl.colors.ListedColormap):
|
|||
----------
|
||||
lab : numpy.ndarray, shape (3)
|
||||
CIE lab values.
|
||||
ref_white : numpy.ndarray, shape (3)
|
||||
Reference white, default value is the standard 2° observer for D65.
|
||||
|
||||
Returns
|
||||
-------
|
||||
|
@ -642,10 +641,10 @@ class Colormap(mpl.colors.ListedColormap):
|
|||
f_x**3. if f_x**3. > _EPS else (116.*f_x-16.)/_KAPPA,
|
||||
((lab[0]+16.)/116.)**3 if lab[0]>_KAPPA*_EPS else lab[0]/_KAPPA,
|
||||
f_z**3. if f_z**3. > _EPS else (116.*f_z-16.)/_KAPPA
|
||||
])*(ref_white if ref_white is not None else _REF_WHITE)
|
||||
])*ref_white
|
||||
|
||||
@staticmethod
|
||||
def _xyz2lab(xyz: np.ndarray, ref_white: np.ndarray = None) -> np.ndarray:
|
||||
def _xyz2lab(xyz: np.ndarray, ref_white: np.ndarray = _REF_WHITE) -> np.ndarray:
|
||||
"""
|
||||
CIE Xyz to CIE Lab.
|
||||
|
||||
|
@ -653,6 +652,8 @@ class Colormap(mpl.colors.ListedColormap):
|
|||
----------
|
||||
xyz : numpy.ndarray, shape (3)
|
||||
CIE Xyz values.
|
||||
ref_white : numpy.ndarray, shape (3)
|
||||
Reference white, default value is the standard 2° observer for D65.
|
||||
|
||||
Returns
|
||||
-------
|
||||
|
@ -664,7 +665,6 @@ class Colormap(mpl.colors.ListedColormap):
|
|||
http://www.brucelindbloom.com/index.html?Eqn_Lab_to_XYZ.html
|
||||
|
||||
"""
|
||||
ref_white = ref_white if ref_white is not None else _REF_WHITE
|
||||
f = np.where(xyz/ref_white > _EPS,(xyz/ref_white)**(1./3.),(_KAPPA*xyz/ref_white+16.)/116.)
|
||||
|
||||
return np.array([
|
||||
|
|
|
@ -114,12 +114,13 @@ class Crystal():
|
|||
|
||||
def __repr__(self):
|
||||
"""Represent."""
|
||||
return '\n'.join([f'Crystal family {self.family}']
|
||||
+ ([] if self.lattice is None else [f'Bravais lattice {self.lattice}']+
|
||||
list(map(lambda x:f'{x[0]}: {x[1]:.5g}',
|
||||
zip(['a','b','c','α','β','γ',],
|
||||
self.parameters))))
|
||||
)
|
||||
family = f'Crystal family: {self.family}'
|
||||
return family if self.lattice is None else \
|
||||
'\n'.join([family,
|
||||
f'Bravais lattice: {self.lattice}',
|
||||
'a={:.5g}m, b={:.5g}m, c={:.5g}m'.format(*self.parameters[:3]),
|
||||
'α={:.5g}°, β={:.5g}°, γ={:.5g}°'.format(*np.degrees(self.parameters[3:]))])
|
||||
|
||||
|
||||
def __eq__(self,other):
|
||||
"""
|
||||
|
@ -543,6 +544,73 @@ class Crystal():
|
|||
[ 1,-2, 1,-3, 1,-2, 1, 2],
|
||||
[ 2,-1,-1,-3, 2,-1,-1, 2]])]
|
||||
},
|
||||
'tI': {
|
||||
'slip': [np.array([
|
||||
[+0,+0,+1, +1,+0,+0],
|
||||
[+0,+0,+1, +0,+1,+0]]),
|
||||
np.array([
|
||||
[+0,+0,+1, +1,+1,+0],
|
||||
[+0,+0,+1, -1,+1,+0]]),
|
||||
np.array([
|
||||
[+0,+1,+0, +1,+0,+0],
|
||||
[+1,+0,+0, +0,+1,+0]]),
|
||||
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]]),
|
||||
np.array([
|
||||
[+1,-1,+0, +1,+1,+0],
|
||||
[+1,+1,+0, +1,-1,+0]]),
|
||||
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]]),
|
||||
np.array([
|
||||
[+0,+1,+0, +0,+0,+1],
|
||||
[+1,+0,+0, +0,+0,+1]]),
|
||||
np.array([
|
||||
[+1,+1,+0, +0,+0,+1],
|
||||
[-1,+1,+0, +0,+0,+1]]),
|
||||
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]]),
|
||||
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]]),
|
||||
np.array([
|
||||
[+1,+0,+0, +0,+1,+1],
|
||||
[+1,+0,+0, +0,+1,-1],
|
||||
[+0,+1,+0, +1,+0,+1],
|
||||
[+0,+1,+0, +1,+0,-1]]),
|
||||
np.array([
|
||||
[+0,+1,-1, +2,+1,+1],
|
||||
[+0,-1,-1, +2,-1,+1],
|
||||
[+1,+0,-1, +1,+2,+1],
|
||||
[-1,+0,-1, -1,+2,+1],
|
||||
[+0,+1,-1, -2,+1,+1],
|
||||
[+0,-1,-1, -2,-1,+1],
|
||||
[-1,+0,-1, -1,-2,+1],
|
||||
[+1,+0,-1, +1,-2,+1]]),
|
||||
np.array([
|
||||
[-1,+1,+1, +2,+1,+1],
|
||||
[-1,-1,+1, +2,-1,+1],
|
||||
[+1,-1,+1, +1,+2,+1],
|
||||
[-1,-1,+1, -1,+2,+1],
|
||||
[+1,+1,+1, -2,+1,+1],
|
||||
[+1,-1,+1, -2,-1,+1],
|
||||
[-1,+1,+1, -1,-2,+1],
|
||||
[+1,+1,+1, +1,-2,+1]])]
|
||||
}
|
||||
}
|
||||
master = _kinematics[self.lattice][mode]
|
||||
if self.lattice == 'hP':
|
||||
|
|
|
@ -514,6 +514,17 @@ class Orientation(Rotation,Crystal):
|
|||
[ 0.07359167 -0.36505797 0.92807163]]
|
||||
Bunge Eulers / deg: (11.40, 21.86, 0.60)
|
||||
|
||||
Plot a sample from the Mackenzie distribution.
|
||||
|
||||
>>> import matplotlib.pyplot as plt
|
||||
>>> import damask
|
||||
>>> N = 10000
|
||||
>>> a = damask.Orientation.from_random(shape=N,family='cubic')
|
||||
>>> b = damask.Orientation.from_random(shape=N,family='cubic')
|
||||
>>> d = a.disorientation(b).as_axis_angle(degrees=True,pair=True)[1]
|
||||
>>> plt.hist(d,25)
|
||||
>>> plt.show()
|
||||
|
||||
"""
|
||||
if self.family != other.family:
|
||||
raise NotImplementedError('disorientation between different crystal families')
|
||||
|
|
|
@ -0,0 +1,11 @@
|
|||
"""Functionality for typehints."""
|
||||
|
||||
from typing import Sequence, Union, TextIO
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
|
||||
|
||||
FloatSequence = Union[np.ndarray,Sequence[float]]
|
||||
IntSequence = Union[np.ndarray,Sequence[int]]
|
||||
FileHandle = Union[TextIO, str, Path]
|
|
@ -12,21 +12,23 @@ the following operations are required for tensorial data:
|
|||
|
||||
"""
|
||||
|
||||
from typing import Sequence, Tuple, Union
|
||||
from typing import Tuple as _Tuple
|
||||
|
||||
from scipy import spatial as _spatial
|
||||
import numpy as _np
|
||||
|
||||
from ._typehints import FloatSequence as _FloatSequence, IntSequence as _IntSequence
|
||||
|
||||
def _ks(size: _np.ndarray, cells: Union[_np.ndarray,Sequence[int]], first_order: bool = False) -> _np.ndarray:
|
||||
|
||||
def _ks(size: _FloatSequence, cells: _IntSequence, first_order: bool = False) -> _np.ndarray:
|
||||
"""
|
||||
Get wave numbers operator.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size of the periodic field.
|
||||
cells : numpy.ndarray of shape (3)
|
||||
cells : sequence of int, len (3)
|
||||
Number of cells.
|
||||
first_order : bool, optional
|
||||
Correction for first order derivatives, defaults to False.
|
||||
|
@ -45,20 +47,20 @@ def _ks(size: _np.ndarray, cells: Union[_np.ndarray,Sequence[int]], first_order:
|
|||
return _np.stack(_np.meshgrid(k_sk,k_sj,k_si,indexing = 'ij'), axis=-1)
|
||||
|
||||
|
||||
def curl(size: _np.ndarray, f: _np.ndarray) -> _np.ndarray:
|
||||
def curl(size: _FloatSequence, f: _np.ndarray) -> _np.ndarray:
|
||||
u"""
|
||||
Calculate curl of a vector or tensor field in Fourier space.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size of the periodic field.
|
||||
f : numpy.ndarray of shape (:,:,:,3) or (:,:,:,3,3)
|
||||
f : numpy.ndarray, shape (:,:,:,3) or (:,:,:,3,3)
|
||||
Periodic field of which the curl is calculated.
|
||||
|
||||
Returns
|
||||
-------
|
||||
∇ × f : numpy.ndarray
|
||||
∇ × f : numpy.ndarray, shape (:,:,:,3) or (:,:,:,3,3)
|
||||
Curl of f.
|
||||
|
||||
"""
|
||||
|
@ -76,20 +78,20 @@ def curl(size: _np.ndarray, f: _np.ndarray) -> _np.ndarray:
|
|||
return _np.fft.irfftn(curl_,axes=(0,1,2),s=f.shape[:3])
|
||||
|
||||
|
||||
def divergence(size: _np.ndarray, f: _np.ndarray) -> _np.ndarray:
|
||||
def divergence(size: _FloatSequence, f: _np.ndarray) -> _np.ndarray:
|
||||
u"""
|
||||
Calculate divergence of a vector or tensor field in Fourier space.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size of the periodic field.
|
||||
f : numpy.ndarray of shape (:,:,:,3) or (:,:,:,3,3)
|
||||
f : numpy.ndarray, shape (:,:,:,3) or (:,:,:,3,3)
|
||||
Periodic field of which the divergence is calculated.
|
||||
|
||||
Returns
|
||||
-------
|
||||
∇ · f : numpy.ndarray
|
||||
∇ · f : numpy.ndarray, shape (:,:,:,1) or (:,:,:,3)
|
||||
Divergence of f.
|
||||
|
||||
"""
|
||||
|
@ -103,20 +105,20 @@ def divergence(size: _np.ndarray, f: _np.ndarray) -> _np.ndarray:
|
|||
return _np.fft.irfftn(div_,axes=(0,1,2),s=f.shape[:3])
|
||||
|
||||
|
||||
def gradient(size: _np.ndarray, f: _np.ndarray) -> _np.ndarray:
|
||||
def gradient(size: _FloatSequence, f: _np.ndarray) -> _np.ndarray:
|
||||
u"""
|
||||
Calculate gradient of a scalar or vector field in Fourier space.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size of the periodic field.
|
||||
f : numpy.ndarray of shape (:,:,:,1) or (:,:,:,3)
|
||||
f : numpy.ndarray, shape (:,:,:,1) or (:,:,:,3)
|
||||
Periodic field of which the gradient is calculated.
|
||||
|
||||
Returns
|
||||
-------
|
||||
∇ f : numpy.ndarray
|
||||
∇ f : numpy.ndarray, shape (:,:,:,3) or (:,:,:,3,3)
|
||||
Divergence of f.
|
||||
|
||||
"""
|
||||
|
@ -130,29 +132,30 @@ def gradient(size: _np.ndarray, f: _np.ndarray) -> _np.ndarray:
|
|||
return _np.fft.irfftn(grad_,axes=(0,1,2),s=f.shape[:3])
|
||||
|
||||
|
||||
def coordinates0_point(cells: Union[ _np.ndarray,Sequence[int]],
|
||||
size: _np.ndarray,
|
||||
origin: _np.ndarray = _np.zeros(3)) -> _np.ndarray:
|
||||
def coordinates0_point(cells: _IntSequence,
|
||||
size: _FloatSequence,
|
||||
origin: _FloatSequence = _np.zeros(3)) -> _np.ndarray:
|
||||
"""
|
||||
Cell center positions (undeformed).
|
||||
|
||||
Parameters
|
||||
----------
|
||||
cells : numpy.ndarray of shape (3)
|
||||
cells : sequence of int, len (3)
|
||||
Number of cells.
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size of the periodic field.
|
||||
origin : numpy.ndarray, optional
|
||||
origin : sequence of float, len(3), optional
|
||||
Physical origin of the periodic field. Defaults to [0.0,0.0,0.0].
|
||||
|
||||
Returns
|
||||
-------
|
||||
x_p_0 : numpy.ndarray
|
||||
x_p_0 : numpy.ndarray, shape (:,:,:,3)
|
||||
Undeformed cell center coordinates.
|
||||
|
||||
"""
|
||||
start = origin + size/_np.array(cells)*.5
|
||||
end = origin + size - size/_np.array(cells)*.5
|
||||
size_ = _np.array(size,float)
|
||||
start = origin + size_/_np.array(cells,int)*.5
|
||||
end = origin + size_ - size_/_np.array(cells,int)*.5
|
||||
|
||||
return _np.stack(_np.meshgrid(_np.linspace(start[0],end[0],cells[0]),
|
||||
_np.linspace(start[1],end[1],cells[1]),
|
||||
|
@ -160,24 +163,24 @@ def coordinates0_point(cells: Union[ _np.ndarray,Sequence[int]],
|
|||
axis = -1)
|
||||
|
||||
|
||||
def displacement_fluct_point(size: _np.ndarray, F: _np.ndarray) -> _np.ndarray:
|
||||
def displacement_fluct_point(size: _FloatSequence, F: _np.ndarray) -> _np.ndarray:
|
||||
"""
|
||||
Cell center displacement field from fluctuation part of the deformation gradient field.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size of the periodic field.
|
||||
F : numpy.ndarray
|
||||
F : numpy.ndarray, shape (:,:,:,3,3)
|
||||
Deformation gradient field.
|
||||
|
||||
Returns
|
||||
-------
|
||||
u_p_fluct : numpy.ndarray
|
||||
u_p_fluct : numpy.ndarray, shape (:,:,:,3)
|
||||
Fluctuating part of the cell center displacements.
|
||||
|
||||
"""
|
||||
integrator = 0.5j*size/_np.pi
|
||||
integrator = 0.5j*_np.array(size,float)/_np.pi
|
||||
|
||||
k_s = _ks(size,F.shape[:3],False)
|
||||
k_s_squared = _np.einsum('...l,...l',k_s,k_s)
|
||||
|
@ -192,20 +195,20 @@ def displacement_fluct_point(size: _np.ndarray, F: _np.ndarray) -> _np.ndarray:
|
|||
return _np.fft.irfftn(displacement,axes=(0,1,2),s=F.shape[:3])
|
||||
|
||||
|
||||
def displacement_avg_point(size: _np.ndarray, F: _np.ndarray) -> _np.ndarray:
|
||||
def displacement_avg_point(size: _FloatSequence, F: _np.ndarray) -> _np.ndarray:
|
||||
"""
|
||||
Cell center displacement field from average part of the deformation gradient field.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size of the periodic field.
|
||||
F : numpy.ndarray
|
||||
F : numpy.ndarray, shape (:,:,:,3,3)
|
||||
Deformation gradient field.
|
||||
|
||||
Returns
|
||||
-------
|
||||
u_p_avg : numpy.ndarray
|
||||
u_p_avg : numpy.ndarray, shape (:,:,:,3)
|
||||
Average part of the cell center displacements.
|
||||
|
||||
"""
|
||||
|
@ -213,42 +216,42 @@ def displacement_avg_point(size: _np.ndarray, F: _np.ndarray) -> _np.ndarray:
|
|||
return _np.einsum('ml,ijkl->ijkm',F_avg - _np.eye(3),coordinates0_point(F.shape[:3],size))
|
||||
|
||||
|
||||
def displacement_point(size: _np.ndarray, F: _np.ndarray) -> _np.ndarray:
|
||||
def displacement_point(size: _FloatSequence, F: _np.ndarray) -> _np.ndarray:
|
||||
"""
|
||||
Cell center displacement field from deformation gradient field.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size of the periodic field.
|
||||
F : numpy.ndarray
|
||||
F : numpy.ndarray, shape (:,:,:,3,3)
|
||||
Deformation gradient field.
|
||||
|
||||
Returns
|
||||
-------
|
||||
u_p : numpy.ndarray
|
||||
u_p : numpy.ndarray, shape (:,:,:,3)
|
||||
Cell center displacements.
|
||||
|
||||
"""
|
||||
return displacement_avg_point(size,F) + displacement_fluct_point(size,F)
|
||||
|
||||
|
||||
def coordinates_point(size: _np.ndarray, F: _np.ndarray, origin: _np.ndarray = _np.zeros(3)) -> _np.ndarray:
|
||||
def coordinates_point(size: _FloatSequence, F: _np.ndarray, origin: _FloatSequence = _np.zeros(3)) -> _np.ndarray:
|
||||
"""
|
||||
Cell center positions.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size of the periodic field.
|
||||
F : numpy.ndarray
|
||||
F : numpy.ndarray, shape (:,:,:,3,3)
|
||||
Deformation gradient field.
|
||||
origin : numpy.ndarray of shape (3), optional
|
||||
origin : sequence of float, len(3), optional
|
||||
Physical origin of the periodic field. Defaults to [0.0,0.0,0.0].
|
||||
|
||||
Returns
|
||||
-------
|
||||
x_p : numpy.ndarray
|
||||
x_p : numpy.ndarray, shape (:,:,:,3)
|
||||
Cell center coordinates.
|
||||
|
||||
"""
|
||||
|
@ -256,14 +259,14 @@ def coordinates_point(size: _np.ndarray, F: _np.ndarray, origin: _np.ndarray = _
|
|||
|
||||
|
||||
def cellsSizeOrigin_coordinates0_point(coordinates0: _np.ndarray,
|
||||
ordered: bool = True) -> Tuple[_np.ndarray,_np.ndarray,_np.ndarray]:
|
||||
ordered: bool = True) -> _Tuple[_np.ndarray,_np.ndarray,_np.ndarray]:
|
||||
"""
|
||||
Return grid 'DNA', i.e. cells, size, and origin from 1D array of point positions.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
coordinates0 : numpy.ndarray of shape (:,3)
|
||||
Undeformed cell coordinates.
|
||||
coordinates0 : numpy.ndarray, shape (:,3)
|
||||
Undeformed cell center coordinates.
|
||||
ordered : bool, optional
|
||||
Expect coordinates0 data to be ordered (x fast, z slow).
|
||||
Defaults to True.
|
||||
|
@ -277,7 +280,7 @@ def cellsSizeOrigin_coordinates0_point(coordinates0: _np.ndarray,
|
|||
coords = [_np.unique(coordinates0[:,i]) for i in range(3)]
|
||||
mincorner = _np.array(list(map(min,coords)))
|
||||
maxcorner = _np.array(list(map(max,coords)))
|
||||
cells = _np.array(list(map(len,coords)),'i')
|
||||
cells = _np.array(list(map(len,coords)),int)
|
||||
size = cells/_np.maximum(cells-1,1) * (maxcorner-mincorner)
|
||||
delta = size/cells
|
||||
origin = mincorner - delta*.5
|
||||
|
@ -305,24 +308,24 @@ def cellsSizeOrigin_coordinates0_point(coordinates0: _np.ndarray,
|
|||
return (cells,size,origin)
|
||||
|
||||
|
||||
def coordinates0_node(cells: Union[_np.ndarray,Sequence[int]],
|
||||
size: _np.ndarray,
|
||||
origin: _np.ndarray = _np.zeros(3)) -> _np.ndarray:
|
||||
def coordinates0_node(cells: _IntSequence,
|
||||
size: _FloatSequence,
|
||||
origin: _FloatSequence = _np.zeros(3)) -> _np.ndarray:
|
||||
"""
|
||||
Nodal positions (undeformed).
|
||||
|
||||
Parameters
|
||||
----------
|
||||
cells : numpy.ndarray of shape (3)
|
||||
cells : sequence of int, len (3)
|
||||
Number of cells.
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size of the periodic field.
|
||||
origin : numpy.ndarray of shape (3), optional
|
||||
origin : sequence of float, len(3), optional
|
||||
Physical origin of the periodic field. Defaults to [0.0,0.0,0.0].
|
||||
|
||||
Returns
|
||||
-------
|
||||
x_n_0 : numpy.ndarray
|
||||
x_n_0 : numpy.ndarray, shape (:,:,:,3)
|
||||
Undeformed nodal coordinates.
|
||||
|
||||
"""
|
||||
|
@ -332,40 +335,40 @@ def coordinates0_node(cells: Union[_np.ndarray,Sequence[int]],
|
|||
axis = -1)
|
||||
|
||||
|
||||
def displacement_fluct_node(size: _np.ndarray, F: _np.ndarray) -> _np.ndarray:
|
||||
def displacement_fluct_node(size: _FloatSequence, F: _np.ndarray) -> _np.ndarray:
|
||||
"""
|
||||
Nodal displacement field from fluctuation part of the deformation gradient field.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size of the periodic field.
|
||||
F : numpy.ndarray
|
||||
F : numpy.ndarray, shape (:,:,:,3,3)
|
||||
Deformation gradient field.
|
||||
|
||||
Returns
|
||||
-------
|
||||
u_n_fluct : numpy.ndarray
|
||||
u_n_fluct : numpy.ndarray, shape (:,:,:,3)
|
||||
Fluctuating part of the nodal displacements.
|
||||
|
||||
"""
|
||||
return point_to_node(displacement_fluct_point(size,F))
|
||||
|
||||
|
||||
def displacement_avg_node(size: _np.ndarray, F: _np.ndarray) -> _np.ndarray:
|
||||
def displacement_avg_node(size: _FloatSequence, F: _np.ndarray) -> _np.ndarray:
|
||||
"""
|
||||
Nodal displacement field from average part of the deformation gradient field.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size of the periodic field.
|
||||
F : numpy.ndarray
|
||||
F : numpy.ndarray, shape (:,:,:,3,3)
|
||||
Deformation gradient field.
|
||||
|
||||
Returns
|
||||
-------
|
||||
u_n_avg : numpy.ndarray
|
||||
u_n_avg : numpy.ndarray, shape (:,:,:,3)
|
||||
Average part of the nodal displacements.
|
||||
|
||||
"""
|
||||
|
@ -373,42 +376,42 @@ def displacement_avg_node(size: _np.ndarray, F: _np.ndarray) -> _np.ndarray:
|
|||
return _np.einsum('ml,ijkl->ijkm',F_avg - _np.eye(3),coordinates0_node(F.shape[:3],size))
|
||||
|
||||
|
||||
def displacement_node(size: _np.ndarray, F: _np.ndarray) -> _np.ndarray:
|
||||
def displacement_node(size: _FloatSequence, F: _np.ndarray) -> _np.ndarray:
|
||||
"""
|
||||
Nodal displacement field from deformation gradient field.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size of the periodic field.
|
||||
F : numpy.ndarray
|
||||
F : numpy.ndarray, shape (:,:,:,3,3)
|
||||
Deformation gradient field.
|
||||
|
||||
Returns
|
||||
-------
|
||||
u_p : numpy.ndarray
|
||||
u_p : numpy.ndarray, shape (:,:,:,3)
|
||||
Nodal displacements.
|
||||
|
||||
"""
|
||||
return displacement_avg_node(size,F) + displacement_fluct_node(size,F)
|
||||
|
||||
|
||||
def coordinates_node(size: _np.ndarray, F: _np.ndarray, origin: _np.ndarray = _np.zeros(3)) -> _np.ndarray:
|
||||
def coordinates_node(size: _FloatSequence, F: _np.ndarray, origin: _FloatSequence = _np.zeros(3)) -> _np.ndarray:
|
||||
"""
|
||||
Nodal positions.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size of the periodic field.
|
||||
F : numpy.ndarray
|
||||
F : numpy.ndarray, shape (:,:,:,3,3)
|
||||
Deformation gradient field.
|
||||
origin : numpy.ndarray of shape (3), optional
|
||||
origin : sequence of float, len(3), optional
|
||||
Physical origin of the periodic field. Defaults to [0.0,0.0,0.0].
|
||||
|
||||
Returns
|
||||
-------
|
||||
x_n : numpy.ndarray
|
||||
x_n : numpy.ndarray, shape (:,:,:,3)
|
||||
Nodal coordinates.
|
||||
|
||||
"""
|
||||
|
@ -416,13 +419,13 @@ def coordinates_node(size: _np.ndarray, F: _np.ndarray, origin: _np.ndarray = _n
|
|||
|
||||
|
||||
def cellsSizeOrigin_coordinates0_node(coordinates0: _np.ndarray,
|
||||
ordered: bool = True) -> Tuple[_np.ndarray,_np.ndarray,_np.ndarray]:
|
||||
ordered: bool = True) -> _Tuple[_np.ndarray,_np.ndarray,_np.ndarray]:
|
||||
"""
|
||||
Return grid 'DNA', i.e. cells, size, and origin from 1D array of nodal positions.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
coordinates0 : numpy.ndarray of shape (:,3)
|
||||
coordinates0 : numpy.ndarray, shape (:,3)
|
||||
Undeformed nodal coordinates.
|
||||
ordered : bool, optional
|
||||
Expect coordinates0 data to be ordered (x fast, z slow).
|
||||
|
@ -437,7 +440,7 @@ def cellsSizeOrigin_coordinates0_node(coordinates0: _np.ndarray,
|
|||
coords = [_np.unique(coordinates0[:,i]) for i in range(3)]
|
||||
mincorner = _np.array(list(map(min,coords)))
|
||||
maxcorner = _np.array(list(map(max,coords)))
|
||||
cells = _np.array(list(map(len,coords)),'i') - 1
|
||||
cells = _np.array(list(map(len,coords)),int) - 1
|
||||
size = maxcorner-mincorner
|
||||
origin = mincorner
|
||||
|
||||
|
@ -463,12 +466,12 @@ def point_to_node(cell_data: _np.ndarray) -> _np.ndarray:
|
|||
|
||||
Parameters
|
||||
----------
|
||||
cell_data : numpy.ndarray of shape (:,:,:,...)
|
||||
cell_data : numpy.ndarray, shape (:,:,:,...)
|
||||
Data defined on the cell centers of a periodic grid.
|
||||
|
||||
Returns
|
||||
-------
|
||||
node_data : numpy.ndarray of shape (:,:,:,...)
|
||||
node_data : numpy.ndarray, shape (:,:,:,...)
|
||||
Data defined on the nodes of a periodic grid.
|
||||
|
||||
"""
|
||||
|
@ -485,12 +488,12 @@ def node_to_point(node_data: _np.ndarray) -> _np.ndarray:
|
|||
|
||||
Parameters
|
||||
----------
|
||||
node_data : numpy.ndarray of shape (:,:,:,...)
|
||||
node_data : numpy.ndarray, shape (:,:,:,...)
|
||||
Data defined on the nodes of a periodic grid.
|
||||
|
||||
Returns
|
||||
-------
|
||||
cell_data : numpy.ndarray of shape (:,:,:,...)
|
||||
cell_data : numpy.ndarray, shape (:,:,:,...)
|
||||
Data defined on the cell centers of a periodic grid.
|
||||
|
||||
"""
|
||||
|
@ -507,7 +510,7 @@ def coordinates0_valid(coordinates0: _np.ndarray) -> bool:
|
|||
|
||||
Parameters
|
||||
----------
|
||||
coordinates0 : numpy.ndarray
|
||||
coordinates0 : numpy.ndarray, shape (:,3)
|
||||
Array of undeformed cell coordinates.
|
||||
|
||||
Returns
|
||||
|
@ -523,17 +526,17 @@ def coordinates0_valid(coordinates0: _np.ndarray) -> bool:
|
|||
return False
|
||||
|
||||
|
||||
def regrid(size: _np.ndarray, F: _np.ndarray, cells: Union[_np.ndarray,Sequence[int]]) -> _np.ndarray:
|
||||
def regrid(size: _FloatSequence, F: _np.ndarray, cells: _IntSequence) -> _np.ndarray:
|
||||
"""
|
||||
Return mapping from coordinates in deformed configuration to a regular grid.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size.
|
||||
F : numpy.ndarray of shape (:,:,:,3,3)
|
||||
F : numpy.ndarray, shape (:,:,:,3,3), shape (:,:,:,3,3)
|
||||
Deformation gradient field.
|
||||
cells : numpy.ndarray of shape (3)
|
||||
cells : sequence of int, len (3)
|
||||
Cell count along x,y,z of remapping grid.
|
||||
|
||||
"""
|
||||
|
|
|
@ -5,7 +5,7 @@ All routines operate on numpy.ndarrays of shape (...,3,3).
|
|||
|
||||
"""
|
||||
|
||||
from typing import Sequence
|
||||
from typing import Sequence as _Sequence
|
||||
|
||||
import numpy as _np
|
||||
|
||||
|
@ -243,7 +243,7 @@ def stretch_right(T: _np.ndarray) -> _np.ndarray:
|
|||
return _polar_decomposition(T,'U')[0]
|
||||
|
||||
|
||||
def _polar_decomposition(T: _np.ndarray, requested: Sequence[str]) -> tuple:
|
||||
def _polar_decomposition(T: _np.ndarray, requested: _Sequence[str]) -> tuple:
|
||||
"""
|
||||
Perform singular value decomposition.
|
||||
|
||||
|
|
|
@ -1,25 +1,27 @@
|
|||
"""Functionality for generation of seed points for Voronoi or Laguerre tessellation."""
|
||||
|
||||
from typing import Sequence,Tuple
|
||||
from typing import Tuple as _Tuple
|
||||
|
||||
from scipy import spatial as _spatial
|
||||
import numpy as _np
|
||||
|
||||
from ._typehints import FloatSequence as _FloatSequence, IntSequence as _IntSequence
|
||||
from . import util as _util
|
||||
from . import grid_filters as _grid_filters
|
||||
|
||||
|
||||
def from_random(size: _np.ndarray, N_seeds: int, cells: _np.ndarray = None, rng_seed=None) -> _np.ndarray:
|
||||
def from_random(size: _FloatSequence, N_seeds: int, cells: _IntSequence = None,
|
||||
rng_seed=None) -> _np.ndarray:
|
||||
"""
|
||||
Place seeds randomly in space.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size of the seeding domain.
|
||||
N_seeds : int
|
||||
Number of seeds.
|
||||
cells : numpy.ndarray of shape (3), optional.
|
||||
cells : sequence of int, len (3), optional.
|
||||
If given, ensures that each seed results in a grain when a standard Voronoi
|
||||
tessellation is performed using the given grid resolution (i.e. size/cells).
|
||||
rng_seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional
|
||||
|
@ -28,29 +30,30 @@ def from_random(size: _np.ndarray, N_seeds: int, cells: _np.ndarray = None, rng_
|
|||
|
||||
Returns
|
||||
-------
|
||||
coords : numpy.ndarray of shape (N_seeds,3)
|
||||
coords : numpy.ndarray, shape (N_seeds,3)
|
||||
Seed coordinates in 3D space.
|
||||
|
||||
"""
|
||||
size_ = _np.array(size,float)
|
||||
rng = _np.random.default_rng(rng_seed)
|
||||
if cells is None:
|
||||
coords = rng.random((N_seeds,3)) * size
|
||||
coords = rng.random((N_seeds,3)) * size_
|
||||
else:
|
||||
grid_coords = _grid_filters.coordinates0_point(cells,size).reshape(-1,3,order='F')
|
||||
coords = grid_coords[rng.choice(_np.prod(cells),N_seeds, replace=False)] \
|
||||
+ _np.broadcast_to(size/cells,(N_seeds,3))*(rng.random((N_seeds,3))*.5-.25) # wobble without leaving cells
|
||||
+ _np.broadcast_to(size_/_np.array(cells,int),(N_seeds,3))*(rng.random((N_seeds,3))*.5-.25) # wobble w/o leaving grid
|
||||
|
||||
return coords
|
||||
|
||||
|
||||
def from_Poisson_disc(size: _np.ndarray, N_seeds: int, N_candidates: int, distance: float,
|
||||
def from_Poisson_disc(size: _FloatSequence, N_seeds: int, N_candidates: int, distance: float,
|
||||
periodic: bool = True, rng_seed=None) -> _np.ndarray:
|
||||
"""
|
||||
Place seeds according to a Poisson disc distribution.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
size : numpy.ndarray of shape (3)
|
||||
size : sequence of float, len (3)
|
||||
Physical size of the seeding domain.
|
||||
N_seeds : int
|
||||
Number of seeds.
|
||||
|
@ -66,13 +69,13 @@ def from_Poisson_disc(size: _np.ndarray, N_seeds: int, N_candidates: int, distan
|
|||
|
||||
Returns
|
||||
-------
|
||||
coords : numpy.ndarray of shape (N_seeds,3)
|
||||
coords : numpy.ndarray, shape (N_seeds,3)
|
||||
Seed coordinates in 3D space.
|
||||
|
||||
"""
|
||||
rng = _np.random.default_rng(rng_seed)
|
||||
coords = _np.empty((N_seeds,3))
|
||||
coords[0] = rng.random(3) * size
|
||||
coords[0] = rng.random(3) * _np.array(size,float)
|
||||
|
||||
s = 1
|
||||
i = 0
|
||||
|
@ -96,8 +99,8 @@ def from_Poisson_disc(size: _np.ndarray, N_seeds: int, N_candidates: int, distan
|
|||
return coords
|
||||
|
||||
|
||||
def from_grid(grid, selection: Sequence[int] = None,
|
||||
invert: bool = False, average: bool = False, periodic: bool = True) -> Tuple[_np.ndarray, _np.ndarray]:
|
||||
def from_grid(grid, selection: _IntSequence = None,
|
||||
invert: bool = False, average: bool = False, periodic: bool = True) -> _Tuple[_np.ndarray, _np.ndarray]:
|
||||
"""
|
||||
Create seeds from grid description.
|
||||
|
||||
|
@ -105,7 +108,7 @@ def from_grid(grid, selection: Sequence[int] = None,
|
|||
----------
|
||||
grid : damask.Grid
|
||||
Grid from which the material IDs are used as seeds.
|
||||
selection : iterable of integers, optional
|
||||
selection : sequence of int, optional
|
||||
Material IDs to consider.
|
||||
invert : boolean, false
|
||||
Consider all material IDs except those in selection. Defaults to False.
|
||||
|
@ -116,7 +119,7 @@ def from_grid(grid, selection: Sequence[int] = None,
|
|||
|
||||
Returns
|
||||
-------
|
||||
coords, materials : numpy.ndarray of shape (:,3), numpy.ndarray of shape (:)
|
||||
coords, materials : numpy.ndarray, shape (:,3); numpy.ndarray, shape (:)
|
||||
Seed coordinates in 3D space, material IDs.
|
||||
|
||||
"""
|
||||
|
|
|
@ -22,7 +22,7 @@ __all__=[
|
|||
'natural_sort',
|
||||
'show_progress',
|
||||
'scale_to_coprime',
|
||||
'project_stereographic',
|
||||
'project_equal_angle', 'project_equal_area',
|
||||
'hybrid_IA',
|
||||
'execution_stamp',
|
||||
'shapeshifter', 'shapeblender',
|
||||
|
@ -267,13 +267,13 @@ def scale_to_coprime(v):
|
|||
return m
|
||||
|
||||
|
||||
def project_stereographic(vector,direction='z',normalize=True,keepdims=False):
|
||||
def project_equal_angle(vector,direction='z',normalize=True,keepdims=False):
|
||||
"""
|
||||
Apply stereographic projection to vector.
|
||||
Apply equal-angle projection to vector.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
vector : numpy.ndarray of shape (...,3)
|
||||
vector : numpy.ndarray, shape (...,3)
|
||||
Vector coordinates to be projected.
|
||||
direction : str
|
||||
Projection direction 'x', 'y', or 'z'.
|
||||
|
@ -281,32 +281,74 @@ def project_stereographic(vector,direction='z',normalize=True,keepdims=False):
|
|||
normalize : bool
|
||||
Ensure unit length of input vector. Defaults to True.
|
||||
keepdims : bool
|
||||
Maintain three-dimensional output coordinates.
|
||||
Default two-dimensional output uses right-handed frame spanned by
|
||||
Maintain three-dimensional output coordinates. Defaults to False.
|
||||
Two-dimensional output uses right-handed frame spanned by
|
||||
the next and next-next axis relative to the projection direction,
|
||||
e.g. x-y when projecting along z and z-x when projecting along y.
|
||||
|
||||
Returns
|
||||
-------
|
||||
coordinates : numpy.ndarray of shape (...,2 | 3)
|
||||
coordinates : numpy.ndarray, shape (...,2 | 3)
|
||||
Projected coordinates.
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> import damask
|
||||
>>> import numpy as np
|
||||
>>> project_stereographic(np.ones(3))
|
||||
>>> project_equal_angle(np.ones(3))
|
||||
[0.3660254, 0.3660254]
|
||||
>>> project_stereographic(np.ones(3),direction='x',normalize=False,keepdims=True)
|
||||
>>> project_equal_angle(np.ones(3),direction='x',normalize=False,keepdims=True)
|
||||
[0, 0.5, 0.5]
|
||||
>>> project_stereographic([0,1,1],direction='y',normalize=True,keepdims=False)
|
||||
>>> project_equal_angle([0,1,1],direction='y',normalize=True,keepdims=False)
|
||||
[0.41421356, 0]
|
||||
|
||||
"""
|
||||
shift = 'zyx'.index(direction)
|
||||
v_ = np.roll(vector/np.linalg.norm(vector,axis=-1,keepdims=True) if normalize else vector,
|
||||
v = np.roll(vector/np.linalg.norm(vector,axis=-1,keepdims=True) if normalize else vector,
|
||||
shift,axis=-1)
|
||||
return np.roll(np.block([v_[...,:2]/(1+np.abs(v_[...,2:3])),np.zeros_like(v_[...,2:3])]),
|
||||
return np.roll(np.block([v[...,:2]/(1.0+np.abs(v[...,2:3])),np.zeros_like(v[...,2:3])]),
|
||||
-shift if keepdims else 0,axis=-1)[...,:3 if keepdims else 2]
|
||||
|
||||
def project_equal_area(vector,direction='z',normalize=True,keepdims=False):
|
||||
"""
|
||||
Apply equal-area projection to vector.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
vector : numpy.ndarray, shape (...,3)
|
||||
Vector coordinates to be projected.
|
||||
direction : str
|
||||
Projection direction 'x', 'y', or 'z'.
|
||||
Defaults to 'z'.
|
||||
normalize : bool
|
||||
Ensure unit length of input vector. Defaults to True.
|
||||
keepdims : bool
|
||||
Maintain three-dimensional output coordinates. Defaults to False.
|
||||
Two-dimensional output uses right-handed frame spanned by
|
||||
the next and next-next axis relative to the projection direction,
|
||||
e.g. x-y when projecting along z and z-x when projecting along y.
|
||||
|
||||
Returns
|
||||
-------
|
||||
coordinates : numpy.ndarray, shape (...,2 | 3)
|
||||
Projected coordinates.
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> import damask
|
||||
>>> import numpy as np
|
||||
>>> project_equal_area(np.ones(3))
|
||||
[0.45970084, 0.45970084]
|
||||
>>> project_equal_area(np.ones(3),direction='x',normalize=False,keepdims=True)
|
||||
[0.0, 0.70710678, 0.70710678]
|
||||
>>> project_equal_area([0,1,1],direction='y',normalize=True,keepdims=False)
|
||||
[0.5411961, 0.0]
|
||||
|
||||
"""
|
||||
shift = 'zyx'.index(direction)
|
||||
v = np.roll(vector/np.linalg.norm(vector,axis=-1,keepdims=True) if normalize else vector,
|
||||
shift,axis=-1)
|
||||
return np.roll(np.block([v[...,:2]/np.sqrt(1.0+np.abs(v[...,2:3])),np.zeros_like(v[...,2:3])]),
|
||||
-shift if keepdims else 0,axis=-1)[...,:3 if keepdims else 2]
|
||||
|
||||
|
||||
|
|
|
@ -79,3 +79,23 @@ class TestCrystal:
|
|||
a=a,b=b,c=c,
|
||||
alpha=alpha,beta=beta,gamma=gamma)
|
||||
assert np.allclose(points,c.lattice_points)
|
||||
|
||||
@pytest.mark.parametrize('crystal,length',
|
||||
[(Crystal(lattice='cF'),[12,6]),
|
||||
(Crystal(lattice='cI'),[12,12,24]),
|
||||
(Crystal(lattice='hP'),[3,3,6,12,6]),
|
||||
(Crystal(lattice='tI',c=1.2),[2,2,2,4,2,4,2,2,4,8,4,8,8])
|
||||
])
|
||||
def test_N_slip(self,crystal,length):
|
||||
assert [len(s) for s in crystal.kinematics('slip')['direction']] == length
|
||||
assert [len(s) for s in crystal.kinematics('slip')['plane']] == length
|
||||
|
||||
@pytest.mark.parametrize('crystal,length',
|
||||
[(Crystal(lattice='cF'),[12]),
|
||||
(Crystal(lattice='cI'),[12]),
|
||||
(Crystal(lattice='hP'),[6,6,6,6]),
|
||||
])
|
||||
def test_N_twin(self,crystal,length):
|
||||
assert [len(s) for s in crystal.kinematics('twin')['direction']] == length
|
||||
assert [len(s) for s in crystal.kinematics('twin')['plane']] == length
|
||||
|
||||
|
|
|
@ -2,6 +2,8 @@ import pytest
|
|||
import numpy as np
|
||||
|
||||
from damask import grid_filters
|
||||
from damask import Grid
|
||||
from damask import seeds
|
||||
|
||||
class TestGridFilters:
|
||||
|
||||
|
@ -139,12 +141,19 @@ class TestGridFilters:
|
|||
else:
|
||||
function(unordered,mode)
|
||||
|
||||
def test_regrid(self):
|
||||
def test_regrid_identity(self):
|
||||
size = np.random.random(3)
|
||||
cells = np.random.randint(8,32,(3))
|
||||
F = np.broadcast_to(np.eye(3), tuple(cells)+(3,3))
|
||||
assert all(grid_filters.regrid(size,F,cells) == np.arange(cells.prod()))
|
||||
|
||||
def test_regrid_double_cells(self):
|
||||
size = np.random.random(3)
|
||||
cells = np.random.randint(8,32,(3))
|
||||
g = Grid.from_Voronoi_tessellation(cells,size,seeds.from_random(size,10))
|
||||
F = np.broadcast_to(np.eye(3), tuple(cells)+(3,3))
|
||||
assert all(g.scale(cells*2).material.flatten() ==
|
||||
g.material.flatten()[grid_filters.regrid(size,F,cells*2)])
|
||||
|
||||
@pytest.mark.parametrize('differential_operator',[grid_filters.curl,
|
||||
grid_filters.divergence,
|
||||
|
|
|
@ -59,8 +59,21 @@ class TestUtil:
|
|||
([1,1,0],'x',False,False,[0.5,0]),
|
||||
([1,1,1],'y',True, True, [0.3660254, 0,0.3660254]),
|
||||
])
|
||||
def test_project_stereographic(self,point,direction,normalize,keepdims,answer):
|
||||
assert np.allclose(util.project_stereographic(np.array(point),direction=direction,
|
||||
def test_project_equal_angle(self,point,direction,normalize,keepdims,answer):
|
||||
assert np.allclose(util.project_equal_angle(np.array(point),direction=direction,
|
||||
normalize=normalize,keepdims=keepdims),answer)
|
||||
|
||||
@pytest.mark.parametrize('point,direction,normalize,keepdims,answer',
|
||||
[
|
||||
([1,0,0],'z',False,True, [1,0,0]),
|
||||
([1,0,0],'z',True, False,[1,0]),
|
||||
([0,1,1],'z',False,True, [0,0.70710678,0]),
|
||||
([0,1,1],'y',True, False,[0.5411961,0]),
|
||||
([1,1,0],'x',False,False,[0.70710678,0]),
|
||||
([1,1,1],'y',True, True, [0.45970084,0,0.45970084]),
|
||||
])
|
||||
def test_project_equal_area(self,point,direction,normalize,keepdims,answer):
|
||||
assert np.allclose(util.project_equal_area(np.array(point),direction=direction,
|
||||
normalize=normalize,keepdims=keepdims),answer)
|
||||
|
||||
@pytest.mark.parametrize('fro,to,mode,answer',
|
||||
|
|
|
@ -6,7 +6,7 @@
|
|||
module LAPACK_interface
|
||||
interface
|
||||
|
||||
subroutine dgeev(jobvl,jobvr,n,a,lda,wr,wi,vl,ldvl,vr,ldvr,work,lwork,info)
|
||||
pure subroutine dgeev(jobvl,jobvr,n,a,lda,wr,wi,vl,ldvl,vr,ldvr,work,lwork,info)
|
||||
use prec
|
||||
character, intent(in) :: jobvl,jobvr
|
||||
integer, intent(in) :: n,lda,ldvl,ldvr,lwork
|
||||
|
@ -18,16 +18,16 @@ module LAPACK_interface
|
|||
integer, intent(out) :: info
|
||||
end subroutine dgeev
|
||||
|
||||
subroutine dgesv(n,nrhs,a,lda,ipiv,b,ldb,info)
|
||||
pure subroutine dgesv(n,nrhs,a,lda,ipiv,b,ldb,info)
|
||||
use prec
|
||||
integer, intent(in) :: n,nrhs,lda,ldb
|
||||
real(pReal), intent(inout), dimension(lda,n) :: a
|
||||
integer, intent(out), dimension(n) :: ipiv
|
||||
real(pReal), intent(out), dimension(ldb,nrhs) :: b
|
||||
real(pReal), intent(inout), dimension(ldb,nrhs) :: b
|
||||
integer, intent(out) :: info
|
||||
end subroutine dgesv
|
||||
|
||||
subroutine dgetrf(m,n,a,lda,ipiv,info)
|
||||
pure subroutine dgetrf(m,n,a,lda,ipiv,info)
|
||||
use prec
|
||||
integer, intent(in) :: m,n,lda
|
||||
real(pReal), intent(inout), dimension(lda,n) :: a
|
||||
|
@ -35,16 +35,16 @@ module LAPACK_interface
|
|||
integer, intent(out) :: info
|
||||
end subroutine dgetrf
|
||||
|
||||
subroutine dgetri(n,a,lda,ipiv,work,lwork,info)
|
||||
pure subroutine dgetri(n,a,lda,ipiv,work,lwork,info)
|
||||
use prec
|
||||
integer, intent(in) :: n,lda,lwork
|
||||
real(pReal), intent(inout), dimension(lda,n) :: a
|
||||
integer, intent(out), dimension(n) :: ipiv
|
||||
integer, intent(in), dimension(n) :: ipiv
|
||||
real(pReal), intent(out), dimension(max(1,lwork)) :: work
|
||||
integer, intent(out) :: info
|
||||
end subroutine dgetri
|
||||
|
||||
subroutine dsyev(jobz,uplo,n,a,lda,w,work,lwork,info)
|
||||
pure subroutine dsyev(jobz,uplo,n,a,lda,w,work,lwork,info)
|
||||
use prec
|
||||
character, intent(in) :: jobz,uplo
|
||||
integer, intent(in) :: n,lda,lwork
|
||||
|
|
|
@ -9,7 +9,8 @@ module constants
|
|||
public
|
||||
|
||||
real(pReal), parameter :: &
|
||||
T_ROOM = 300.0_pReal, & !< Room temperature in K
|
||||
K_B = 1.38e-23_pReal !< Boltzmann constant in J/Kelvin
|
||||
T_ROOM = 300.0_pReal, & !< Room temperature in K. ToDo: IUPAC: 298.15
|
||||
K_B = 1.38e-23_pReal, & !< Boltzmann constant in J/Kelvin
|
||||
N_A = 6.02214076e23_pReal !< Avogadro constant in 1/mol
|
||||
|
||||
end module constants
|
||||
|
|
|
@ -2070,7 +2070,7 @@ end function getlabels
|
|||
!> @brief Equivalent Poisson's ratio (ν)
|
||||
!> @details https://doi.org/10.1143/JPSJ.20.635
|
||||
!--------------------------------------------------------------------------------------------------
|
||||
function lattice_equivalent_nu(C,assumption) result(nu)
|
||||
pure function lattice_equivalent_nu(C,assumption) result(nu)
|
||||
|
||||
real(pReal), dimension(6,6), intent(in) :: C !< Stiffness tensor (Voigt notation)
|
||||
character(len=5), intent(in) :: assumption !< Assumption ('Voigt' = isostrain, 'Reuss' = isostress)
|
||||
|
@ -2103,7 +2103,7 @@ end function lattice_equivalent_nu
|
|||
!> @brief Equivalent shear modulus (μ)
|
||||
!> @details https://doi.org/10.1143/JPSJ.20.635
|
||||
!--------------------------------------------------------------------------------------------------
|
||||
function lattice_equivalent_mu(C,assumption) result(mu)
|
||||
pure function lattice_equivalent_mu(C,assumption) result(mu)
|
||||
|
||||
real(pReal), dimension(6,6), intent(in) :: C !< Stiffness tensor (Voigt notation)
|
||||
character(len=5), intent(in) :: assumption !< Assumption ('Voigt' = isostrain, 'Reuss' = isostress)
|
||||
|
|
22
src/math.f90
22
src/math.f90
|
@ -512,7 +512,7 @@ end subroutine math_invert33
|
|||
!--------------------------------------------------------------------------------------------------
|
||||
!> @brief Inversion of symmetriced 3x3x3x3 matrix
|
||||
!--------------------------------------------------------------------------------------------------
|
||||
function math_invSym3333(A)
|
||||
pure function math_invSym3333(A)
|
||||
|
||||
real(pReal),dimension(3,3,3,3) :: math_invSym3333
|
||||
|
||||
|
@ -538,7 +538,7 @@ end function math_invSym3333
|
|||
!--------------------------------------------------------------------------------------------------
|
||||
!> @brief invert quadratic matrix of arbitrary dimension
|
||||
!--------------------------------------------------------------------------------------------------
|
||||
subroutine math_invert(InvA, error, A)
|
||||
pure subroutine math_invert(InvA, error, A)
|
||||
|
||||
real(pReal), dimension(:,:), intent(in) :: A
|
||||
real(pReal), dimension(size(A,1),size(A,1)), intent(out) :: invA
|
||||
|
@ -996,7 +996,7 @@ end subroutine math_normal
|
|||
!--------------------------------------------------------------------------------------------------
|
||||
!> @brief eigenvalues and eigenvectors of symmetric matrix
|
||||
!--------------------------------------------------------------------------------------------------
|
||||
subroutine math_eigh(w,v,error,m)
|
||||
pure subroutine math_eigh(w,v,error,m)
|
||||
|
||||
real(pReal), dimension(:,:), intent(in) :: m !< quadratic matrix to compute eigenvectors and values of
|
||||
real(pReal), dimension(size(m,1)), intent(out) :: w !< eigenvalues
|
||||
|
@ -1021,7 +1021,7 @@ end subroutine math_eigh
|
|||
!> @author Martin Diehl, Max-Planck-Institut für Eisenforschung GmbH
|
||||
!> @details See http://arxiv.org/abs/physics/0610206 (DSYEVH3)
|
||||
!--------------------------------------------------------------------------------------------------
|
||||
subroutine math_eigh33(w,v,m)
|
||||
pure subroutine math_eigh33(w,v,m)
|
||||
|
||||
real(pReal), dimension(3,3),intent(in) :: m !< 3x3 matrix to compute eigenvectors and values of
|
||||
real(pReal), dimension(3), intent(out) :: w !< eigenvalues
|
||||
|
@ -1114,7 +1114,7 @@ end function math_rotationalPart
|
|||
!> @brief Eigenvalues of symmetric matrix
|
||||
! will return NaN on error
|
||||
!--------------------------------------------------------------------------------------------------
|
||||
function math_eigvalsh(m)
|
||||
pure function math_eigvalsh(m)
|
||||
|
||||
real(pReal), dimension(:,:), intent(in) :: m !< symmetric matrix to compute eigenvalues of
|
||||
real(pReal), dimension(size(m,1)) :: math_eigvalsh
|
||||
|
@ -1137,7 +1137,7 @@ end function math_eigvalsh
|
|||
!> but apparently more stable solution and has general LAPACK powered version for arbritrary sized
|
||||
!> matrices as fallback
|
||||
!--------------------------------------------------------------------------------------------------
|
||||
function math_eigvalsh33(m)
|
||||
pure function math_eigvalsh33(m)
|
||||
|
||||
real(pReal), intent(in), dimension(3,3) :: m !< 3x3 symmetric matrix to compute eigenvalues of
|
||||
real(pReal), dimension(3) :: math_eigvalsh33,I
|
||||
|
@ -1432,9 +1432,11 @@ subroutine selfTest
|
|||
error stop 'math_LeviCivita'
|
||||
|
||||
normal_distribution: block
|
||||
real(pReal), dimension(500000) :: r
|
||||
integer, parameter :: N = 1000000
|
||||
real(pReal), dimension(:), allocatable :: r
|
||||
real(pReal) :: mu, sigma
|
||||
|
||||
allocate(r(N))
|
||||
call random_number(mu)
|
||||
call random_number(sigma)
|
||||
|
||||
|
@ -1443,11 +1445,11 @@ subroutine selfTest
|
|||
|
||||
call math_normal(r,mu,sigma)
|
||||
|
||||
if (abs(mu -sum(r)/real(size(r),pReal))>5.0e-2_pReal) &
|
||||
if (abs(mu -sum(r)/real(N,pReal))>5.0e-2_pReal) &
|
||||
error stop 'math_normal(mu)'
|
||||
|
||||
mu = sum(r)/real(size(r),pReal)
|
||||
if (abs(sigma**2 -1.0_pReal/real(size(r)-1,pReal) * sum((r-mu)**2))/sigma > 5.0e-2_pReal) &
|
||||
mu = sum(r)/real(N,pReal)
|
||||
if (abs(sigma**2 -1.0_pReal/real(N-1,pReal) * sum((r-mu)**2))/sigma > 5.0e-2_pReal) &
|
||||
error stop 'math_normal(sigma)'
|
||||
end block normal_distribution
|
||||
|
||||
|
|
|
@ -168,17 +168,17 @@ submodule(phase) mechanical
|
|||
integer, intent(in) :: ph,en
|
||||
end function plastic_dislotwin_homogenizedC
|
||||
|
||||
module function elastic_C66(ph,en) result(C66)
|
||||
pure module function elastic_C66(ph,en) result(C66)
|
||||
real(pReal), dimension(6,6) :: C66
|
||||
integer, intent(in) :: ph, en
|
||||
end function elastic_C66
|
||||
|
||||
module function elastic_mu(ph,en) result(mu)
|
||||
pure module function elastic_mu(ph,en) result(mu)
|
||||
real(pReal) :: mu
|
||||
integer, intent(in) :: ph, en
|
||||
end function elastic_mu
|
||||
|
||||
module function elastic_nu(ph,en) result(nu)
|
||||
pure module function elastic_nu(ph,en) result(nu)
|
||||
real(pReal) :: nu
|
||||
integer, intent(in) :: ph, en
|
||||
end function elastic_nu
|
||||
|
|
|
@ -30,7 +30,7 @@ module subroutine elastic_init(phases)
|
|||
phase, &
|
||||
mech, &
|
||||
elastic
|
||||
logical :: thermal_active
|
||||
|
||||
|
||||
print'(/,1x,a)', '<<<+- phase:mechanical:elastic init -+>>>'
|
||||
print'(/,1x,a)', '<<<+- phase:mechanical:elastic:Hooke init -+>>>'
|
||||
|
@ -86,7 +86,7 @@ end subroutine elastic_init
|
|||
!--------------------------------------------------------------------------------------------------
|
||||
!> @brief return 6x6 elasticity tensor
|
||||
!--------------------------------------------------------------------------------------------------
|
||||
module function elastic_C66(ph,en) result(C66)
|
||||
pure module function elastic_C66(ph,en) result(C66)
|
||||
|
||||
integer, intent(in) :: &
|
||||
ph, &
|
||||
|
@ -140,7 +140,7 @@ end function elastic_C66
|
|||
!--------------------------------------------------------------------------------------------------
|
||||
!> @brief return shear modulus
|
||||
!--------------------------------------------------------------------------------------------------
|
||||
module function elastic_mu(ph,en) result(mu)
|
||||
pure module function elastic_mu(ph,en) result(mu)
|
||||
|
||||
integer, intent(in) :: &
|
||||
ph, &
|
||||
|
@ -157,7 +157,7 @@ end function elastic_mu
|
|||
!--------------------------------------------------------------------------------------------------
|
||||
!> @brief return Poisson ratio
|
||||
!--------------------------------------------------------------------------------------------------
|
||||
module function elastic_nu(ph,en) result(nu)
|
||||
pure module function elastic_nu(ph,en) result(nu)
|
||||
|
||||
integer, intent(in) :: &
|
||||
ph, &
|
||||
|
|
|
@ -86,6 +86,8 @@ module function plastic_kinehardening_init() result(myPlasticity)
|
|||
print'(/,1x,a)', '<<<+- phase:mechanical:plastic:kinehardening init -+>>>'
|
||||
print'(/,a,i0)', ' # phases: ',count(myPlasticity); flush(IO_STDOUT)
|
||||
|
||||
print'(/,1x,a)', 'J.A. Wollmershauser et al., International Journal of Fatigue 36:181–193, 2012'
|
||||
print'( 1x,a)', 'https://doi.org/10.1016/j.ijfatigue.2011.07.008'
|
||||
|
||||
phases => config_material%get('phase')
|
||||
allocate(param(phases%length))
|
||||
|
|
|
@ -372,7 +372,7 @@ end function rotTensor4
|
|||
|
||||
|
||||
!---------------------------------------------------------------------------------------------------
|
||||
!> @brief Rotate a rank-4 tensor in Voigt 6x6 notation passively (default) or actively.
|
||||
!> @brief Rotate a rank-4 stiffness tensor in Voigt 6x6 notation passively (default) or actively.
|
||||
!> @details: https://scicomp.stackexchange.com/questions/35600
|
||||
!! ToDo: Need to check active/passive !!!
|
||||
!---------------------------------------------------------------------------------------------------
|
||||
|
@ -393,11 +393,11 @@ pure function rotStiffness(self,C,active) result(cRot)
|
|||
R = self%asMatrix()
|
||||
endif
|
||||
|
||||
M = reshape([R(1,1)**2.0_pReal, R(2,1)**2.0_pReal, R(3,1)**2.0_pReal, &
|
||||
M = reshape([R(1,1)**2, R(2,1)**2, R(3,1)**2, &
|
||||
R(2,1)*R(3,1), R(1,1)*R(3,1), R(1,1)*R(2,1), &
|
||||
R(1,2)**2.0_pReal, R(2,2)**2.0_pReal, R(3,2)**2.0_pReal, &
|
||||
R(1,2)**2, R(2,2)**2, R(3,2)**2, &
|
||||
R(2,2)*R(3,2), R(1,2)*R(3,2), R(1,2)*R(2,2), &
|
||||
R(1,3)**2.0_pReal, R(2,3)**2.0_pReal, R(3,3)**2.0_pReal, &
|
||||
R(1,3)**2, R(2,3)**2, R(3,3)**2, &
|
||||
R(2,3)*R(3,3), R(1,3)*R(3,3), R(1,3)*R(2,3), &
|
||||
2.0_pReal*R(1,2)*R(1,3), 2.0_pReal*R(2,2)*R(2,3), 2.0_pReal*R(3,2)*R(3,3), &
|
||||
R(2,2)*R(3,3)+R(2,3)*R(3,2), R(1,2)*R(3,3)+R(1,3)*R(3,2), R(1,2)*R(2,3)+R(1,3)*R(2,2), &
|
||||
|
|
Loading…
Reference in New Issue