parallelize addition of datasets
threads does not work, muliprocessing shows good performance: Overhead is small compared to the performance gain. Especially useful for long running functions of the orientation class
This commit is contained in:
Martin Diehl
parent
b9966b95e0
commit
05476ff85c
|
|
@ -1,7 +1,8 @@
|
|||
from queue import Queue
|
||||
import multiprocessing
|
||||
import re
|
||||
import glob
|
||||
import os
|
||||
from functools import partial
|
||||
|
||||
import vtk
|
||||
from vtk.util import numpy_support
|
||||
|
|
@ -443,6 +444,17 @@ class DADF5():
|
|||
return f['geometry/x_c'][()]
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_absolute(x):
|
||||
return {
|
||||
'data': np.abs(x['data']),
|
||||
'label': '|{}|'.format(x['label']),
|
||||
'meta': {
|
||||
'Unit': x['meta']['Unit'],
|
||||
'Description': 'Absolute value of {} ({})'.format(x['label'],x['meta']['Description']),
|
||||
'Creator': 'dadf5.py:add_abs v{}'.format(version)
|
||||
}
|
||||
}
|
||||
def add_absolute(self,x):
|
||||
"""
|
||||
Add absolute value.
|
||||
|
|
@ -453,21 +465,24 @@ class DADF5():
|
|||
Label of scalar, vector, or tensor dataset to take absolute value of.
|
||||
|
||||
"""
|
||||
def _add_absolute(x):
|
||||
|
||||
return {
|
||||
'data': np.abs(x['data']),
|
||||
'label': '|{}|'.format(x['label']),
|
||||
'meta': {
|
||||
'Unit': x['meta']['Unit'],
|
||||
'Description': 'Absolute value of {} ({})'.format(x['label'],x['meta']['Description']),
|
||||
'Creator': 'dadf5.py:add_abs v{}'.format(version)
|
||||
}
|
||||
}
|
||||
|
||||
self.__add_generic_pointwise(_add_absolute,{'x':x})
|
||||
self.__add_generic_pointwise(self._add_absolute,{'x':x})
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_calculation(**kwargs):
|
||||
formula = kwargs['formula']
|
||||
for d in re.findall(r'#(.*?)#',formula):
|
||||
formula = formula.replace('#{}#'.format(d),"kwargs['{}']['data']".format(d))
|
||||
|
||||
return {
|
||||
'data': eval(formula),
|
||||
'label': kwargs['label'],
|
||||
'meta': {
|
||||
'Unit': kwargs['unit'],
|
||||
'Description': '{} (formula: {})'.format(kwargs['description'],kwargs['formula']),
|
||||
'Creator': 'dadf5.py:add_calculation v{}'.format(version)
|
||||
}
|
||||
}
|
||||
def add_calculation(self,label,formula,unit='n/a',description=None,vectorized=True):
|
||||
"""
|
||||
Add result of a general formula.
|
||||
|
|
@ -489,28 +504,24 @@ class DADF5():
|
|||
if not vectorized:
|
||||
raise NotImplementedError
|
||||
|
||||
def _add_calculation(**kwargs):
|
||||
|
||||
formula = kwargs['formula']
|
||||
for d in re.findall(r'#(.*?)#',formula):
|
||||
formula = formula.replace('#{}#'.format(d),"kwargs['{}']['data']".format(d))
|
||||
|
||||
return {
|
||||
'data': eval(formula),
|
||||
'label': kwargs['label'],
|
||||
'meta': {
|
||||
'Unit': kwargs['unit'],
|
||||
'Description': '{} (formula: {})'.format(kwargs['description'],kwargs['formula']),
|
||||
'Creator': 'dadf5.py:add_calculation v{}'.format(version)
|
||||
}
|
||||
}
|
||||
|
||||
dataset_mapping = {d:d for d in set(re.findall(r'#(.*?)#',formula))} # datasets used in the formula
|
||||
dataset_mapping = {d:d for d in set(re.findall(r'#(.*?)#',formula))} # datasets used in the formula
|
||||
args = {'formula':formula,'label':label,'unit':unit,'description':description}
|
||||
|
||||
self.__add_generic_pointwise(_add_calculation,dataset_mapping,args)
|
||||
self.__add_generic_pointwise(self._add_calculation,dataset_mapping,args)
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_Cauchy(P,F):
|
||||
return {
|
||||
'data': mechanics.Cauchy(P['data'],F['data']),
|
||||
'label': 'sigma',
|
||||
'meta': {
|
||||
'Unit': P['meta']['Unit'],
|
||||
'Description': 'Cauchy stress calculated from {} ({}) '.format(P['label'],
|
||||
P['meta']['Description'])+\
|
||||
'and {} ({})'.format(F['label'],F['meta']['Description']),
|
||||
'Creator': 'dadf5.py:add_Cauchy v{}'.format(version)
|
||||
}
|
||||
}
|
||||
def add_Cauchy(self,P='P',F='F'):
|
||||
"""
|
||||
Add Cauchy stress calculated from first Piola-Kirchhoff stress and deformation gradient.
|
||||
|
|
@ -523,23 +534,20 @@ class DADF5():
|
|||
Label of the dataset containing the deformation gradient. Defaults to ‘F’.
|
||||
|
||||
"""
|
||||
def _add_Cauchy(P,F):
|
||||
|
||||
return {
|
||||
'data': mechanics.Cauchy(P['data'],F['data']),
|
||||
'label': 'sigma',
|
||||
'meta': {
|
||||
'Unit': P['meta']['Unit'],
|
||||
'Description': 'Cauchy stress calculated from {} ({}) '.format(P['label'],
|
||||
P['meta']['Description'])+\
|
||||
'and {} ({})'.format(F['label'],F['meta']['Description']),
|
||||
'Creator': 'dadf5.py:add_Cauchy v{}'.format(version)
|
||||
}
|
||||
}
|
||||
|
||||
self.__add_generic_pointwise(_add_Cauchy,{'P':P,'F':F})
|
||||
self.__add_generic_pointwise(self._add_Cauchy,{'P':P,'F':F})
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_determinant(T):
|
||||
return {
|
||||
'data': np.linalg.det(T['data']),
|
||||
'label': 'det({})'.format(T['label']),
|
||||
'meta': {
|
||||
'Unit': T['meta']['Unit'],
|
||||
'Description': 'Determinant of tensor {} ({})'.format(T['label'],T['meta']['Description']),
|
||||
'Creator': 'dadf5.py:add_determinant v{}'.format(version)
|
||||
}
|
||||
}
|
||||
def add_determinant(self,T):
|
||||
"""
|
||||
Add the determinant of a tensor.
|
||||
|
|
@ -550,21 +558,23 @@ class DADF5():
|
|||
Label of tensor dataset.
|
||||
|
||||
"""
|
||||
def _add_determinant(T):
|
||||
|
||||
return {
|
||||
'data': np.linalg.det(T['data']),
|
||||
'label': 'det({})'.format(T['label']),
|
||||
'meta': {
|
||||
'Unit': T['meta']['Unit'],
|
||||
'Description': 'Determinant of tensor {} ({})'.format(T['label'],T['meta']['Description']),
|
||||
'Creator': 'dadf5.py:add_determinant v{}'.format(version)
|
||||
}
|
||||
}
|
||||
|
||||
self.__add_generic_pointwise(_add_determinant,{'T':T})
|
||||
self.__add_generic_pointwise(self._add_determinant,{'T':T})
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_deviator(T):
|
||||
if not np.all(np.array(T['data'].shape[1:]) == np.array([3,3])):
|
||||
raise ValueError
|
||||
|
||||
return {
|
||||
'data': mechanics.deviatoric_part(T['data']),
|
||||
'label': 's_{}'.format(T['label']),
|
||||
'meta': {
|
||||
'Unit': T['meta']['Unit'],
|
||||
'Description': 'Deviator of tensor {} ({})'.format(T['label'],T['meta']['Description']),
|
||||
'Creator': 'dadf5.py:add_deviator v{}'.format(version)
|
||||
}
|
||||
}
|
||||
def add_deviator(self,T):
|
||||
"""
|
||||
Add the deviatoric part of a tensor.
|
||||
|
|
@ -575,24 +585,20 @@ class DADF5():
|
|||
Label of tensor dataset.
|
||||
|
||||
"""
|
||||
def _add_deviator(T):
|
||||
|
||||
if not np.all(np.array(T['data'].shape[1:]) == np.array([3,3])):
|
||||
raise ValueError
|
||||
|
||||
return {
|
||||
'data': mechanics.deviatoric_part(T['data']),
|
||||
'label': 's_{}'.format(T['label']),
|
||||
'meta': {
|
||||
'Unit': T['meta']['Unit'],
|
||||
'Description': 'Deviator of tensor {} ({})'.format(T['label'],T['meta']['Description']),
|
||||
'Creator': 'dadf5.py:add_deviator v{}'.format(version)
|
||||
}
|
||||
}
|
||||
|
||||
self.__add_generic_pointwise(_add_deviator,{'T':T})
|
||||
self.__add_generic_pointwise(self._add_deviator,{'T':T})
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_eigenvalue(S):
|
||||
return {
|
||||
'data': mechanics.eigenvalues(S['data']),
|
||||
'label': 'lambda({})'.format(S['label']),
|
||||
'meta' : {
|
||||
'Unit': S['meta']['Unit'],
|
||||
'Description': 'Eigenvalues of {} ({})'.format(S['label'],S['meta']['Description']),
|
||||
'Creator': 'dadf5.py:add_eigenvalues v{}'.format(version)
|
||||
}
|
||||
}
|
||||
def add_eigenvalues(self,S):
|
||||
"""
|
||||
Add eigenvalues of symmetric tensor.
|
||||
|
|
@ -603,21 +609,20 @@ class DADF5():
|
|||
Label of symmetric tensor dataset.
|
||||
|
||||
"""
|
||||
def _add_eigenvalue(S):
|
||||
|
||||
return {
|
||||
'data': mechanics.eigenvalues(S['data']),
|
||||
'label': 'lambda({})'.format(S['label']),
|
||||
'meta' : {
|
||||
'Unit': S['meta']['Unit'],
|
||||
'Description': 'Eigenvalues of {} ({})'.format(S['label'],S['meta']['Description']),
|
||||
'Creator': 'dadf5.py:add_eigenvalues v{}'.format(version)
|
||||
}
|
||||
}
|
||||
|
||||
self.__add_generic_pointwise(_add_eigenvalue,{'S':S})
|
||||
self.__add_generic_pointwise(self._add_eigenvalue,{'S':S})
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_eigenvector(S):
|
||||
return {
|
||||
'data': mechanics.eigenvectors(S['data']),
|
||||
'label': 'v({})'.format(S['label']),
|
||||
'meta' : {
|
||||
'Unit': '1',
|
||||
'Description': 'Eigenvectors of {} ({})'.format(S['label'],S['meta']['Description']),
|
||||
'Creator': 'dadf5.py:add_eigenvectors v{}'.format(version)
|
||||
}
|
||||
}
|
||||
def add_eigenvectors(self,S):
|
||||
"""
|
||||
Add eigenvectors of symmetric tensor.
|
||||
|
|
@ -628,21 +633,32 @@ class DADF5():
|
|||
Label of symmetric tensor dataset.
|
||||
|
||||
"""
|
||||
def _add_eigenvector(S):
|
||||
|
||||
return {
|
||||
'data': mechanics.eigenvectors(S['data']),
|
||||
'label': 'v({})'.format(S['label']),
|
||||
'meta' : {
|
||||
'Unit': '1',
|
||||
'Description': 'Eigenvectors of {} ({})'.format(S['label'],S['meta']['Description']),
|
||||
'Creator': 'dadf5.py:add_eigenvectors v{}'.format(version)
|
||||
}
|
||||
}
|
||||
|
||||
self.__add_generic_pointwise(_add_eigenvector,{'S':S})
|
||||
self.__add_generic_pointwise(self._add_eigenvector,{'S':S})
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_IPFcolor(q,l):
|
||||
d = np.array(l)
|
||||
d_unit = d/np.linalg.norm(d)
|
||||
m = util.scale_to_coprime(d)
|
||||
colors = np.empty((len(q['data']),3),np.uint8)
|
||||
|
||||
lattice = q['meta']['Lattice']
|
||||
|
||||
for i,q in enumerate(q['data']):
|
||||
o = Orientation(np.array([q['w'],q['x'],q['y'],q['z']]),lattice).reduced()
|
||||
colors[i] = np.uint8(o.IPFcolor(d_unit)*255)
|
||||
|
||||
return {
|
||||
'data': colors,
|
||||
'label': 'IPFcolor_[{} {} {}]'.format(*m),
|
||||
'meta' : {
|
||||
'Unit': 'RGB (8bit)',
|
||||
'Lattice': lattice,
|
||||
'Description': 'Inverse Pole Figure (IPF) colors for direction/plane [{} {} {})'.format(*m),
|
||||
'Creator': 'dadf5.py:add_IPFcolor v{}'.format(version)
|
||||
}
|
||||
}
|
||||
def add_IPFcolor(self,q,l):
|
||||
"""
|
||||
Add RGB color tuple of inverse pole figure (IPF) color.
|
||||
|
|
@ -655,33 +671,20 @@ class DADF5():
|
|||
Lab frame direction for inverse pole figure.
|
||||
|
||||
"""
|
||||
def _add_IPFcolor(q,l):
|
||||
self.__add_generic_pointwise(self._add_IPFcolor,{'q':q},{'l':l})
|
||||
|
||||
d = np.array(l)
|
||||
d_unit = d/np.linalg.norm(d)
|
||||
m = util.scale_to_coprime(d)
|
||||
colors = np.empty((len(q['data']),3),np.uint8)
|
||||
|
||||
lattice = q['meta']['Lattice']
|
||||
|
||||
for i,q in enumerate(q['data']):
|
||||
o = Orientation(np.array([q['w'],q['x'],q['y'],q['z']]),lattice).reduced()
|
||||
colors[i] = np.uint8(o.IPFcolor(d_unit)*255)
|
||||
|
||||
return {
|
||||
'data': colors,
|
||||
'label': 'IPFcolor_[{} {} {}]'.format(*m),
|
||||
'meta' : {
|
||||
'Unit': 'RGB (8bit)',
|
||||
'Lattice': lattice,
|
||||
'Description': 'Inverse Pole Figure (IPF) colors for direction/plane [{} {} {})'.format(*m),
|
||||
'Creator': 'dadf5.py:add_IPFcolor v{}'.format(version)
|
||||
}
|
||||
@staticmethod
|
||||
def _add_maximum_shear(S):
|
||||
return {
|
||||
'data': mechanics.maximum_shear(S['data']),
|
||||
'label': 'max_shear({})'.format(S['label']),
|
||||
'meta': {
|
||||
'Unit': S['meta']['Unit'],
|
||||
'Description': 'Maximum shear component of {} ({})'.format(S['label'],S['meta']['Description']),
|
||||
'Creator': 'dadf5.py:add_maximum_shear v{}'.format(version)
|
||||
}
|
||||
}
|
||||
|
||||
self.__add_generic_pointwise(_add_IPFcolor,{'q':q},{'l':l})
|
||||
|
||||
|
||||
def add_maximum_shear(self,S):
|
||||
"""
|
||||
Add maximum shear components of symmetric tensor.
|
||||
|
|
@ -692,21 +695,23 @@ class DADF5():
|
|||
Label of symmetric tensor dataset.
|
||||
|
||||
"""
|
||||
def _add_maximum_shear(S):
|
||||
|
||||
return {
|
||||
'data': mechanics.maximum_shear(S['data']),
|
||||
'label': 'max_shear({})'.format(S['label']),
|
||||
'meta': {
|
||||
'Unit': S['meta']['Unit'],
|
||||
'Description': 'Maximum shear component of {} ({})'.format(S['label'],S['meta']['Description']),
|
||||
'Creator': 'dadf5.py:add_maximum_shear v{}'.format(version)
|
||||
}
|
||||
}
|
||||
|
||||
self.__add_generic_pointwise(_add_maximum_shear,{'S':S})
|
||||
self.__add_generic_pointwise(self._add_maximum_shear,{'S':S})
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_Mises(S):
|
||||
t = 'strain' if S['meta']['Unit'] == '1' else \
|
||||
'stress'
|
||||
|
||||
return {
|
||||
'data': mechanics.Mises_strain(S['data']) if t=='strain' else mechanics.Mises_stress(S['data']),
|
||||
'label': '{}_vM'.format(S['label']),
|
||||
'meta': {
|
||||
'Unit': S['meta']['Unit'],
|
||||
'Description': 'Mises equivalent {} of {} ({})'.format(t,S['label'],S['meta']['Description']),
|
||||
'Creator': 'dadf5.py:add_Mises v{}'.format(version)
|
||||
}
|
||||
}
|
||||
def add_Mises(self,S):
|
||||
"""
|
||||
Add the equivalent Mises stress or strain of a symmetric tensor.
|
||||
|
|
@ -717,23 +722,32 @@ class DADF5():
|
|||
Label of symmetric tensorial stress or strain dataset.
|
||||
|
||||
"""
|
||||
def _add_Mises(S):
|
||||
|
||||
t = 'strain' if S['meta']['Unit'] == '1' else \
|
||||
'stress'
|
||||
return {
|
||||
'data': mechanics.Mises_strain(S['data']) if t=='strain' else mechanics.Mises_stress(S['data']),
|
||||
'label': '{}_vM'.format(S['label']),
|
||||
'meta': {
|
||||
'Unit': S['meta']['Unit'],
|
||||
'Description': 'Mises equivalent {} of {} ({})'.format(t,S['label'],S['meta']['Description']),
|
||||
'Creator': 'dadf5.py:add_Mises v{}'.format(version)
|
||||
}
|
||||
}
|
||||
|
||||
self.__add_generic_pointwise(_add_Mises,{'S':S})
|
||||
self.__add_generic_pointwise(self._add_Mises,{'S':S})
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_norm(x,ord):
|
||||
o = ord
|
||||
if len(x['data'].shape) == 2:
|
||||
axis = 1
|
||||
t = 'vector'
|
||||
if o is None: o = 2
|
||||
elif len(x['data'].shape) == 3:
|
||||
axis = (1,2)
|
||||
t = 'tensor'
|
||||
if o is None: o = 'fro'
|
||||
else:
|
||||
raise ValueError
|
||||
|
||||
return {
|
||||
'data': np.linalg.norm(x['data'],ord=o,axis=axis,keepdims=True),
|
||||
'label': '|{}|_{}'.format(x['label'],o),
|
||||
'meta': {
|
||||
'Unit': x['meta']['Unit'],
|
||||
'Description': '{}-norm of {} {} ({})'.format(ord,t,x['label'],x['meta']['Description']),
|
||||
'Creator': 'dadf5.py:add_norm v{}'.format(version)
|
||||
}
|
||||
}
|
||||
def add_norm(self,x,ord=None):
|
||||
"""
|
||||
Add the norm of vector or tensor.
|
||||
|
|
@ -746,36 +760,25 @@ class DADF5():
|
|||
Order of the norm. inf means NumPy’s inf object. For details refer to numpy.linalg.norm.
|
||||
|
||||
"""
|
||||
def _add_norm(x,ord):
|
||||
|
||||
o = ord
|
||||
if len(x['data'].shape) == 2:
|
||||
axis = 1
|
||||
t = 'vector'
|
||||
if o is None: o = 2
|
||||
elif len(x['data'].shape) == 3:
|
||||
axis = (1,2)
|
||||
t = 'tensor'
|
||||
if o is None: o = 'fro'
|
||||
else:
|
||||
raise ValueError
|
||||
|
||||
return {
|
||||
'data': np.linalg.norm(x['data'],ord=o,axis=axis,keepdims=True),
|
||||
'label': '|{}|_{}'.format(x['label'],o),
|
||||
'meta': {
|
||||
'Unit': x['meta']['Unit'],
|
||||
'Description': '{}-norm of {} {} ({})'.format(ord,t,x['label'],x['meta']['Description']),
|
||||
'Creator': 'dadf5.py:add_norm v{}'.format(version)
|
||||
}
|
||||
}
|
||||
|
||||
self.__add_generic_pointwise(_add_norm,{'x':x},{'ord':ord})
|
||||
self.__add_generic_pointwise(self._add_norm,{'x':x},{'ord':ord})
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_PK2(P,F):
|
||||
return {
|
||||
'data': mechanics.PK2(P['data'],F['data']),
|
||||
'label': 'S',
|
||||
'meta': {
|
||||
'Unit': P['meta']['Unit'],
|
||||
'Description': '2. Kirchhoff stress calculated from {} ({}) '.format(P['label'],
|
||||
P['meta']['Description'])+\
|
||||
'and {} ({})'.format(F['label'],F['meta']['Description']),
|
||||
'Creator': 'dadf5.py:add_PK2 v{}'.format(version)
|
||||
}
|
||||
}
|
||||
def add_PK2(self,P='P',F='F'):
|
||||
"""
|
||||
Add 2. Piola-Kirchhoff calculated from first Piola-Kirchhoff stress and deformation gradient.
|
||||
Add second Piola-Kirchhoff calculated from first Piola-Kirchhoff stress and deformation gradient.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
|
|
@ -785,23 +788,32 @@ class DADF5():
|
|||
Label of deformation gradient dataset. Defaults to ‘F’.
|
||||
|
||||
"""
|
||||
def _add_PK2(P,F):
|
||||
|
||||
return {
|
||||
'data': mechanics.PK2(P['data'],F['data']),
|
||||
'label': 'S',
|
||||
'meta': {
|
||||
'Unit': P['meta']['Unit'],
|
||||
'Description': '2. Kirchhoff stress calculated from {} ({}) '.format(P['label'],
|
||||
P['meta']['Description'])+\
|
||||
'and {} ({})'.format(F['label'],F['meta']['Description']),
|
||||
'Creator': 'dadf5.py:add_PK2 v{}'.format(version)
|
||||
}
|
||||
}
|
||||
|
||||
self.__add_generic_pointwise(_add_PK2,{'P':P,'F':F})
|
||||
self.__add_generic_pointwise(self._add_PK2,{'P':P,'F':F})
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_pole(q,p,polar):
|
||||
pole = np.array(p)
|
||||
unit_pole = pole/np.linalg.norm(pole)
|
||||
m = util.scale_to_coprime(pole)
|
||||
coords = np.empty((len(q['data']),2))
|
||||
|
||||
for i,q in enumerate(q['data']):
|
||||
o = Rotation(np.array([q['w'],q['x'],q['y'],q['z']]))
|
||||
rotatedPole = o*unit_pole # rotate pole according to crystal orientation
|
||||
(x,y) = rotatedPole[0:2]/(1.+abs(unit_pole[2])) # stereographic projection
|
||||
coords[i] = [np.sqrt(x*x+y*y),np.arctan2(y,x)] if polar else [x,y]
|
||||
|
||||
return {
|
||||
'data': coords,
|
||||
'label': 'p^{}_[{} {} {})'.format(u'rφ' if polar else 'xy',*m),
|
||||
'meta' : {
|
||||
'Unit': '1',
|
||||
'Description': '{} coordinates of stereographic projection of pole (direction/plane) in crystal frame'\
|
||||
.format('Polar' if polar else 'Cartesian'),
|
||||
'Creator' : 'dadf5.py:add_pole v{}'.format(version)
|
||||
}
|
||||
}
|
||||
def add_pole(self,q,p,polar=False):
|
||||
"""
|
||||
Add coordinates of stereographic projection of given pole in crystal frame.
|
||||
|
|
@ -816,33 +828,22 @@ class DADF5():
|
|||
Give pole in polar coordinates. Defaults to False.
|
||||
|
||||
"""
|
||||
def _add_pole(q,p,polar):
|
||||
self.__add_generic_pointwise(self._add_pole,{'q':q},{'p':p,'polar':polar})
|
||||
|
||||
pole = np.array(p)
|
||||
unit_pole = pole/np.linalg.norm(pole)
|
||||
m = util.scale_to_coprime(pole)
|
||||
coords = np.empty((len(q['data']),2))
|
||||
|
||||
for i,q in enumerate(q['data']):
|
||||
o = Rotation(np.array([q['w'],q['x'],q['y'],q['z']]))
|
||||
rotatedPole = o*unit_pole # rotate pole according to crystal orientation
|
||||
(x,y) = rotatedPole[0:2]/(1.+abs(unit_pole[2])) # stereographic projection
|
||||
coords[i] = [np.sqrt(x*x+y*y),np.arctan2(y,x)] if polar else [x,y]
|
||||
|
||||
return {
|
||||
'data': coords,
|
||||
'label': 'p^{}_[{} {} {})'.format(u'rφ' if polar else 'xy',*m),
|
||||
'meta' : {
|
||||
'Unit': '1',
|
||||
'Description': '{} coordinates of stereographic projection of pole (direction/plane) in crystal frame'\
|
||||
.format('Polar' if polar else 'Cartesian'),
|
||||
'Creator' : 'dadf5.py:add_pole v{}'.format(version)
|
||||
}
|
||||
@staticmethod
|
||||
def _add_rotational_part(F):
|
||||
if not np.all(np.array(F['data'].shape[1:]) == np.array([3,3])):
|
||||
raise ValueError
|
||||
return {
|
||||
'data': mechanics.rotational_part(F['data']),
|
||||
'label': 'R({})'.format(F['label']),
|
||||
'meta': {
|
||||
'Unit': F['meta']['Unit'],
|
||||
'Description': 'Rotational part of {} ({})'.format(F['label'],F['meta']['Description']),
|
||||
'Creator': 'dadf5.py:add_rotational_part v{}'.format(version)
|
||||
}
|
||||
}
|
||||
|
||||
self.__add_generic_pointwise(_add_pole,{'q':q},{'p':p,'polar':polar})
|
||||
|
||||
|
||||
def add_rotational_part(self,F):
|
||||
"""
|
||||
Add rotational part of a deformation gradient.
|
||||
|
|
@ -853,21 +854,24 @@ class DADF5():
|
|||
Label of deformation gradient dataset.
|
||||
|
||||
"""
|
||||
def _add_rotational_part(F):
|
||||
|
||||
return {
|
||||
'data': mechanics.rotational_part(F['data']),
|
||||
'label': 'R({})'.format(F['label']),
|
||||
'meta': {
|
||||
'Unit': F['meta']['Unit'],
|
||||
'Description': 'Rotational part of {} ({})'.format(F['label'],F['meta']['Description']),
|
||||
'Creator': 'dadf5.py:add_rotational_part v{}'.format(version)
|
||||
}
|
||||
}
|
||||
|
||||
self.__add_generic_pointwise(_add_rotational_part,{'F':F})
|
||||
self.__add_generic_pointwise(self._add_rotational_part,{'F':F})
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_spherical(T):
|
||||
if not np.all(np.array(T['data'].shape[1:]) == np.array([3,3])):
|
||||
raise ValueError
|
||||
|
||||
return {
|
||||
'data': mechanics.spherical_part(T['data']),
|
||||
'label': 'p_{}'.format(T['label']),
|
||||
'meta': {
|
||||
'Unit': T['meta']['Unit'],
|
||||
'Description': 'Spherical component of tensor {} ({})'.format(T['label'],T['meta']['Description']),
|
||||
'Creator': 'dadf5.py:add_spherical v{}'.format(version)
|
||||
}
|
||||
}
|
||||
def add_spherical(self,T):
|
||||
"""
|
||||
Add the spherical (hydrostatic) part of a tensor.
|
||||
|
|
@ -878,24 +882,22 @@ class DADF5():
|
|||
Label of tensor dataset.
|
||||
|
||||
"""
|
||||
def _add_spherical(T):
|
||||
self.__add_generic_pointwise(self._add_spherical,{'T':T})
|
||||
|
||||
if not np.all(np.array(T['data'].shape[1:]) == np.array([3,3])):
|
||||
|
||||
@staticmethod
|
||||
def _add_strain_tensor(F,t,m):
|
||||
if not np.all(np.array(F['data'].shape[1:]) == np.array([3,3])):
|
||||
raise ValueError
|
||||
|
||||
return {
|
||||
'data': mechanics.spherical_part(T['data']),
|
||||
'label': 'p_{}'.format(T['label']),
|
||||
'meta': {
|
||||
'Unit': T['meta']['Unit'],
|
||||
'Description': 'Spherical component of tensor {} ({})'.format(T['label'],T['meta']['Description']),
|
||||
'Creator': 'dadf5.py:add_spherical v{}'.format(version)
|
||||
}
|
||||
}
|
||||
|
||||
self.__add_generic_pointwise(_add_spherical,{'T':T})
|
||||
|
||||
|
||||
return {
|
||||
'data': mechanics.strain_tensor(F['data'],t,m),
|
||||
'label': 'epsilon_{}^{}({})'.format(t,m,F['label']),
|
||||
'meta': {
|
||||
'Unit': F['meta']['Unit'],
|
||||
'Description': 'Strain tensor of {} ({})'.format(F['label'],F['meta']['Description']),
|
||||
'Creator': 'dadf5.py:add_strain_tensor v{}'.format(version)
|
||||
}
|
||||
}
|
||||
def add_strain_tensor(self,F='F',t='V',m=0.0):
|
||||
"""
|
||||
Add strain tensor of a deformation gradient.
|
||||
|
|
@ -913,21 +915,24 @@ class DADF5():
|
|||
Order of the strain calculation. Defaults to ‘0.0’.
|
||||
|
||||
"""
|
||||
def _add_strain_tensor(F,t,m):
|
||||
|
||||
return {
|
||||
'data': mechanics.strain_tensor(F['data'],t,m),
|
||||
'label': 'epsilon_{}^{}({})'.format(t,m,F['label']),
|
||||
'meta': {
|
||||
'Unit': F['meta']['Unit'],
|
||||
'Description': 'Strain tensor of {} ({})'.format(F['label'],F['meta']['Description']),
|
||||
'Creator': 'dadf5.py:add_strain_tensor v{}'.format(version)
|
||||
}
|
||||
}
|
||||
|
||||
self.__add_generic_pointwise(_add_strain_tensor,{'F':F},{'t':t,'m':m})
|
||||
self.__add_generic_pointwise(self._add_strain_tensor,{'F':F},{'t':t,'m':m})
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _add_stretch_tensor(F,t):
|
||||
if not np.all(np.array(F['data'].shape[1:]) == np.array([3,3])):
|
||||
raise ValueError
|
||||
|
||||
return {
|
||||
'data': mechanics.left_stretch(F['data']) if t == 'V' else mechanics.right_stretch(F['data']),
|
||||
'label': '{}({})'.format(t,F['label']),
|
||||
'meta': {
|
||||
'Unit': F['meta']['Unit'],
|
||||
'Description': '{} stretch tensor of {} ({})'.format('Left' if t == 'V' else 'Right',
|
||||
F['label'],F['meta']['Description']),
|
||||
'Creator': 'dadf5.py:add_stretch_tensor v{}'.format(version)
|
||||
}
|
||||
}
|
||||
def add_stretch_tensor(self,F='F',t='V'):
|
||||
"""
|
||||
Add stretch tensor of a deformation gradient.
|
||||
|
|
@ -941,77 +946,54 @@ class DADF5():
|
|||
Defaults to ‘V’.
|
||||
|
||||
"""
|
||||
def _add_stretch_tensor(F,t):
|
||||
|
||||
return {
|
||||
'data': mechanics.left_stretch(F['data']) if t == 'V' else mechanics.right_stretch(F['data']),
|
||||
'label': '{}({})'.format(t,F['label']),
|
||||
'meta': {
|
||||
'Unit': F['meta']['Unit'],
|
||||
'Description': '{} stretch tensor of {} ({})'.format('Left' if t == 'V' else 'Right',
|
||||
F['label'],F['meta']['Description']),
|
||||
'Creator': 'dadf5.py:add_stretch_tensor v{}'.format(version)
|
||||
}
|
||||
}
|
||||
|
||||
self.__add_generic_pointwise(_add_stretch_tensor,{'F':F},{'t':t})
|
||||
self.__add_generic_pointwise(self._add_stretch_tensor,{'F':F},{'t':t})
|
||||
|
||||
|
||||
def __add_generic_pointwise(self,func,dataset_mapping,args={}):
|
||||
"""
|
||||
General function to add pointwise data.
|
||||
def job(self,group,func,datasets,args,lock):
|
||||
try:
|
||||
d = self._read(group,datasets,lock)
|
||||
r = func(**d,**args)
|
||||
return [group,r]
|
||||
except Exception as err:
|
||||
print('Error during calculation: {}.'.format(err))
|
||||
return None
|
||||
|
||||
Parameters
|
||||
----------
|
||||
func : function
|
||||
Function that calculates a new dataset from one or more datasets per HDF5 group.
|
||||
dataset_mapping : dictionary
|
||||
Mapping HDF5 data label to callback function argument
|
||||
extra_args : dictionary, optional
|
||||
Any extra arguments parsed to func.
|
||||
|
||||
"""
|
||||
def job(args):
|
||||
"""Call function with input data + extra arguments, returns results + group."""
|
||||
args['results'].put({**args['func'](**args['in']),'group':args['group']})
|
||||
def _read(self,group,datasets,lock):
|
||||
datasets_in = {}
|
||||
lock.acquire()
|
||||
with h5py.File(self.fname,'r') as f:
|
||||
for k,v in datasets.items():
|
||||
loc = f[group+'/'+v]
|
||||
datasets_in[k]={'data':loc[()],
|
||||
'label':v,
|
||||
'meta':{k2:v2.decode() for k2,v2 in loc.attrs.items()}}
|
||||
lock.release()
|
||||
return datasets_in
|
||||
|
||||
env = Environment()
|
||||
N_threads = int(env.options['DAMASK_NUM_THREADS'])
|
||||
N_threads //=N_threads # disable for the moment
|
||||
def __add_generic_pointwise(self,func,datasets,args={}):
|
||||
|
||||
results = Queue(N_threads)
|
||||
pool = util.ThreadPool(N_threads)
|
||||
N_added = N_threads + 1
|
||||
env = Environment()
|
||||
N_threads = int(env.options['DAMASK_NUM_THREADS'])
|
||||
pool = multiprocessing.Pool(N_threads)
|
||||
m = multiprocessing.Manager()
|
||||
lock = m.Lock()
|
||||
|
||||
todo = []
|
||||
# ToDo: It would be more memory efficient to read only from file when required, i.e. do to it in pool.add_task
|
||||
for group in self.groups_with_datasets(dataset_mapping.values()):
|
||||
with h5py.File(self.fname,'r') as f:
|
||||
datasets_in = {}
|
||||
for arg,label in dataset_mapping.items():
|
||||
loc = f[group+'/'+label]
|
||||
data = loc[()]
|
||||
meta = {k:loc.attrs[k].decode() for k in loc.attrs.keys()}
|
||||
datasets_in[arg] = {'data': data, 'meta': meta, 'label': label}
|
||||
|
||||
todo.append({'in':{**datasets_in,**args},'func':func,'group':group,'results':results})
|
||||
|
||||
pool.map(job, todo[:N_added]) # initialize
|
||||
|
||||
N_not_calculated = len(todo)
|
||||
while N_not_calculated > 0:
|
||||
result = results.get()
|
||||
with h5py.File(self.fname,'a') as f: # write to file
|
||||
dataset_out = f[result['group']].create_dataset(result['label'],data=result['data'])
|
||||
for k in result['meta'].keys():
|
||||
dataset_out.attrs[k] = result['meta'][k].encode()
|
||||
N_not_calculated-=1
|
||||
|
||||
if N_added < len(todo): # add more jobs
|
||||
pool.add_task(job,todo[N_added])
|
||||
N_added +=1
|
||||
|
||||
pool.wait_completion()
|
||||
groups = self.groups_with_datasets(datasets.values())
|
||||
default_arg = partial(self.job,func=func,datasets=datasets,args=args,lock=lock)
|
||||
for result in pool.imap_unordered(default_arg,groups):
|
||||
if not result: continue
|
||||
lock.acquire()
|
||||
with h5py.File(self.fname, 'a') as f:
|
||||
try:
|
||||
dataset = f[result[0]].create_dataset(result[1]['label'],data=result[1]['data'])
|
||||
for l,v in result[1]['meta'].items():
|
||||
dataset.attrs[l]=v.encode()
|
||||
except OSError as err:
|
||||
print('Could not add dataset: {}.'.format(err))
|
||||
lock.release()
|
||||
pool.close()
|
||||
pool.join()
|
||||
|
||||
|
||||
def to_vtk(self,labels,mode='cell'):
|
||||
|
|
|
|||
|
|
@ -201,57 +201,3 @@ class return_message():
|
|||
def __repr__(self):
|
||||
"""Return message suitable for interactive shells."""
|
||||
return srepr(self.message)
|
||||
|
||||
|
||||
class ThreadPool:
|
||||
"""Pool of threads consuming tasks from a queue."""
|
||||
|
||||
class Worker(Thread):
|
||||
"""Thread executing tasks from a given tasks queue."""
|
||||
|
||||
def __init__(self, tasks):
|
||||
"""Worker for tasks."""
|
||||
Thread.__init__(self)
|
||||
self.tasks = tasks
|
||||
self.daemon = True
|
||||
self.start()
|
||||
|
||||
def run(self):
|
||||
while True:
|
||||
func, args, kargs = self.tasks.get()
|
||||
try:
|
||||
func(*args, **kargs)
|
||||
except Exception as e:
|
||||
# An exception happened in this thread
|
||||
print(e)
|
||||
finally:
|
||||
# Mark this task as done, whether an exception happened or not
|
||||
self.tasks.task_done()
|
||||
|
||||
|
||||
def __init__(self, num_threads):
|
||||
"""
|
||||
Thread pool.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
num_threads : int
|
||||
number of threads
|
||||
|
||||
"""
|
||||
self.tasks = Queue(num_threads)
|
||||
for _ in range(num_threads):
|
||||
self.Worker(self.tasks)
|
||||
|
||||
def add_task(self, func, *args, **kargs):
|
||||
"""Add a task to the queue."""
|
||||
self.tasks.put((func, args, kargs))
|
||||
|
||||
def map(self, func, args_list):
|
||||
"""Add a list of tasks to the queue."""
|
||||
for args in args_list:
|
||||
self.add_task(func, args)
|
||||
|
||||
def wait_completion(self):
|
||||
"""Wait for completion of all the tasks in the queue."""
|
||||
self.tasks.join()
|
||||
|
|
|
|||
Loading…
Reference in New Issue