506 lines
18 KiB
Python
506 lines
18 KiB
Python
from queue import Queue
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import re
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import h5py
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import numpy as np
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from . import util
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# ------------------------------------------------------------------
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class DADF5():
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"""
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Read and write to DADF5 files.
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DADF5 files contain DAMASK results.
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"""
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# ------------------------------------------------------------------
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def __init__(self,filename):
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"""
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Opens an existing DADF5 file.
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Parameters
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----------
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filename : str
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name of the DADF5 file to be openend.
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"""
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with h5py.File(filename,'r') as f:
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if f.attrs['DADF5-major'] != 0 or f.attrs['DADF5-minor'] != 2:
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raise TypeError('Unsupported DADF5 version {} '.format(f.attrs['DADF5-version']))
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self.structured = 'grid' in f['geometry'].attrs.keys()
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if self.structured:
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self.grid = f['geometry'].attrs['grid']
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self.size = f['geometry'].attrs['size']
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r=re.compile('inc[0-9]+')
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self.increments = [{'inc': int(u[3:]),
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'time': round(f[u].attrs['time/s'],12),
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} for u in f.keys() if r.match(u)]
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self.Nmaterialpoints, self.Nconstituents = np.shape(f['mapping/cellResults/constituent'])
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self.materialpoints = [m.decode() for m in np.unique(f['mapping/cellResults/materialpoint']['Name'])]
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self.constituents = [c.decode() for c in np.unique(f['mapping/cellResults/constituent'] ['Name'])]
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self.c_output_types = []
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for c in self.constituents:
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for o in f['inc{:05}/constituent/{}'.format(self.increments[0]['inc'],c)].keys():
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self.c_output_types.append(o)
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self.c_output_types = list(set(self.c_output_types)) # make unique
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self.m_output_types = []
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for m in self.materialpoints:
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for o in f['inc{:05}/materialpoint/{}'.format(self.increments[0]['inc'],m)].keys():
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self.m_output_types.append(o)
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self.m_output_types = list(set(self.m_output_types)) # make unique
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#self.on_air
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self.active= {'increments': self.increments, # ToDo:simplify, activity only positions that translate into (no complex types)
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'constituents': self.constituents,
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'materialpoints': self.materialpoints,
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'constituent': range(self.Nconstituents), # ToDo: stupid naming
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'c_output_types': self.c_output_types,
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'm_output_types': self.m_output_types}
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self.filename = filename
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def __on_air_set(self,output,t,p):
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valid = set(p)
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choice = [output] if isinstance(output,str) else output
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self.active[t] = list(valid.intersection(choice))
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def __on_air_add(self,output,t,p):
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choice = [output] if isinstance(output,str) else output
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valid = set(p).intersection(choice)
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existing = set(self.active[t])
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self.active[t] = list(existing.add(valid))
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def __on_air_del(self,output,t):
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choice = [output] if isinstance(output,str) else output
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existing = set(self.active[t])
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self.active[t] = list(existing.remove(choice))
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def constitutive_output_set(self,output):
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self.__on_air_set(output,'c_output_types',self.c_output_types)
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def constitutive_output_add(self,output):
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self.__on_air_add(output,'c_output_types',self.c_output_types)
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def constitutive_output_del(self,output):
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self.__on_air_del(output,'c_output_types')
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def materialpoint_output_set(self,output):
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self.__on_air_set(output,'m_output_types',self.m_output_types)
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def materialpoint_output_add(self,output):
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self.__on_air_add(output,'m_output_types',self.m_output_types)
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def materialpoint_output_del(self,output):
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self.__on_air_del(output,'m_output_types')
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def constitutive_set(self,output):
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self.__on_air_set(output,'constituents',self.constituents)
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def constitutive_add(self,output):
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self.__on_air_add(output,'constituents',self.constituents)
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def constitutive_del(self,output):
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self.__on_air_del(output,'constituents')
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def materialpoint_set(self,output):
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self.__on_air_set(output,'materialpoints',self.materialpoints)
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def materialpoint_add(self,output):
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self.__on_air_add(output,'materialpoints',self.materialpoints)
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def materialpoint_del(self,output):
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self.__on_air_del(output,'materialpoints')
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def c_it(self):
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a = self.active['constituents']#.copy()
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for i in a:
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self.constitutive_set(i)
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yield i
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self.constitutive_set(a)
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# ToDo: store increments, select icrements (trivial), position, and time
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def get_groups(self,l): #group_with_data(datasets)
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"""
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Get groups that contain all requested datasets.
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Parameters
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----------
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l : list of str
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Names of datasets that need to be located in the group.
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"""
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groups = []
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if type(l) is not list:
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raise TypeError('Candidates should be given as a list')
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with h5py.File(self.filename,'r') as f:
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for g in self.get_active_groups():
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if set(l).issubset(f[g].keys()): groups.append(g)
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return groups
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def get_active_groups(self): # rename: get_groups needed? merge with datasets and have [] and ['*']
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"""Get groups that are currently considered for evaluation."""
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groups = []
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for i in self.active['increments']:
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group_inc = 'inc{:05}'.format(i['inc']) #ToDo: Merge path only once at the end '/'.join(listE)
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for c in self.active['constituents']:
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for t in self.active['c_output_types']:
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groups.append('/'.join([group_inc,'constituent',c,t]))
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for m in self.active['materialpoints']:
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for t in self.active['m_output_types']:
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groups.append('/'.join([group_inc,'materialpoint',m,t]))
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return groups
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def list_data(self): # print_datasets and have [] and ['*'], loop over all increment, soll auf anderen basieren (get groups with sternchen)
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"""Shows information on all active datasets in the file."""
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with h5py.File(self.filename,'r') as f:
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group_inc = 'inc{:05}'.format(self.active['increments'][0]['inc']) #ToDo: Merge path only once at the end '/'.join(listE)
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for c in self.active['constituents']:
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print('\n'+c)
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group_constituent = group_inc+'/constituent/'+c
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for t in self.active['c_output_types']:
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print(' {}'.format(t))
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group_output_types = group_constituent+'/'+t
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try:
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for x in f[group_output_types].keys():
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print(' {} ({})'.format(x,f[group_output_types+'/'+x].attrs['Description'].decode()))
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except KeyError:
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pass
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for m in self.active['materialpoints']:
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group_materialpoint = group_inc+'/materialpoint/'+m
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for t in self.active['m_output_types']:
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print(' {}'.format(t))
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group_output_types = group_materialpoint+'/'+t
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try:
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for x in f[group_output_types].keys():
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print(' {} ({})'.format(x,f[group_output_types+'/'+x].attrs['Description'].decode()))
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except KeyError:
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pass
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def get_dataset_location(self,label): # names
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"""Returns the location of all active datasets with given label.""" #ToDo: Merge path only once at the end '/'.join(listE)
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path = []
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with h5py.File(self.filename,'r') as f:
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for i in self.active['increments']:
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group_inc = 'inc{:05}'.format(i['inc'])
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for c in self.active['constituents']:
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for t in self.active['c_output_types']:
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try:
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p = '/'.join([group_inc,'constituent',c,t,label])
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f[p]
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path.append(p)
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except KeyError as e:
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print('unable to locate constituents dataset: '+ str(e))
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for m in self.active['materialpoints']:
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for t in self.active['m_output_types']:
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try:
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p = '/'.join([group_inc,'materialpoint',m,t,label])
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f[p]
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path.append(p)
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except KeyError as e:
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print('unable to locate materialpoints dataset: '+ str(e))
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return path
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def read_dataset(self,path,c):
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"""
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Dataset for all points/cells.
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If more than one path is given, the dataset is composed of the individual contributions
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"""
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with h5py.File(self.filename,'r') as f:
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shape = (self.Nmaterialpoints,) + np.shape(f[path[0]])[1:]
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if len(shape) == 1: shape = shape +(1,)
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dataset = np.full(shape,np.nan)
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for pa in path:
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label = pa.split('/')[2]
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p = np.where(f['mapping/cellResults/constituent'][:,c]['Name'] == str.encode(label))[0]
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if len(p)>0:
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u = (f['mapping/cellResults/constituent'][p,c]['Position'])
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a = np.array(f[pa])
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if len(a.shape) == 1:
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a=a.reshape([a.shape[0],1])
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dataset[p,:] = a[u,:]
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p = np.where(f['mapping/cellResults/materialpoint']['Name'] == str.encode(label))[0]
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if len(p)>0:
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u = (f['mapping/cellResults/materialpoint'][p.tolist()]['Position'])
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a = np.array(f[pa])
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if len(a.shape) == 1:
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a=a.reshape([a.shape[0],1])
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dataset[p,:] = a[u,:]
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return dataset
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def add_Cauchy(self,P='P',F='F'):
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"""
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Adds Cauchy stress calculated from 1st Piola-Kirchhoff stress and deformation gradient.
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Resulting tensor is symmetrized as the Cauchy stress should be symmetric.
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"""
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def Cauchy(F,P):
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sigma = np.einsum('i,ijk,ilk->ijl',1.0/np.linalg.det(F['data']),P['data'],F['data'])
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sigma = (sigma + np.einsum('ikj',sigma))*0.5 # enforce symmetry
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return {
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'data' : sigma,
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'label' : 'sigma',
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'meta' : {
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'Unit' : P['meta']['Unit'],
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'Description' : 'Cauchy stress calculated from {} ({}) '.format(P['label'],P['meta']['Description'])+\
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'and deformation gradient {} ({})'.format(F['label'],P['meta']['Description']),
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'Creator' : 'dadf5.py:add_Cauchy vXXXXX'
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}
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}
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requested = [{'label':F,'arg':'F'},
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{'label':P,'arg':'P'} ]
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self.__add_generic_pointwise(Cauchy,requested)
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def add_Mises(self,x):
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"""Adds the equivalent Mises stress or strain of a tensor."""
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def deviator(x):
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if x['meta']['Unit'] == 'Pa': #ToDo: Should we use this? Then add_Cauchy and add_strain_tensors also should perform sanity checks
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factor = 3.0/2.0
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elif x['meta']['Unit'] == '-':
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factor = 2.0/3.0
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else:
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ValueError
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d = x['data']
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dev = d - np.einsum('ijk,i->ijk',np.broadcast_to(np.eye(3),[d.shape[0],3,3]),np.trace(d,axis1=1,axis2=2)/3.0)
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#dev_sym = (dev + np.einsum('ikj',dev))*0.5 # ToDo: this is not needed (only if the input is not symmetric, but then the whole concept breaks down)
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return {
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'data' : np.sqrt(np.einsum('ijk->i',dev**2)*factor),
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'label' : 'dev({})'.format(x['label']),
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'meta' : {
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'Unit' : x['meta']['Unit'],
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'Description' : 'Mises equivalent stress of {} ({})'.format(x['label'],x['meta']['Description']),
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'Creator' : 'dadf5.py:add_Mises_stress vXXXXX'
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}
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}
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requested = [{'label':x,'arg':'x'}]
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self.__add_generic_pointwise(deviator,requested)
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def add_norm(self,x,ord=None):
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"""
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Adds norm of vector or tensor or magnitude of a scalar.
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See numpy.linalg.norm manual for details.
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"""
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def norm(x,ord):
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if len(x['data'].shape) == 1:
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axis = 0
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t = 'scalar'
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elif len(x['data'].shape) == 2:
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axis = 1
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t = 'vector'
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elif len(x['data'].shape) == 3:
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axis = (1,2)
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t = 'tensor'
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else:
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raise ValueError
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return {
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'data' : np.linalg.norm(x['data'],ord=ord,axis=axis,keepdims=True),
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'label' : 'norm({})'.format(x['label']),
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'meta' : {
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'Unit' : x['meta']['Unit'],
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'Description' : 'Norm of {} {} ({})'.format(t,x['label'],x['meta']['Description']), # ToDo: add details about norm used
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'Creator' : 'dadf5.py:add_norm vXXXXX'
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}
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}
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requested = [{'label':x,'arg':'x'}]
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self.__add_generic_pointwise(norm,requested,{'ord':ord})
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def add_determinant(self,x):
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"""Adds the determinant component of a tensor."""
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def determinant(x):
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return {
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'data' : np.linalg.det(x['data']),
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'label' : 'det({})'.format(x['label']),
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'meta' : {
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'Unit' : x['meta']['Unit'],
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'Description' : 'Determinant of tensor {} ({})'.format(x['label'],x['meta']['Description']),
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'Creator' : 'dadf5.py:add_determinant vXXXXX'
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}
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}
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requested = [{'label':x,'arg':'x'}]
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self.__add_generic_pointwise(determinant,requested)
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def add_spherical(self,x):
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"""Adds the spherical component of a tensor."""
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def spherical(x):
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if not np.all(np.array(x['data'].shape[1:]) == np.array([3,3])):
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raise ValueError
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return {
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'data' : np.trace(x['data'],axis1=1,axis2=2)/3.0,
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'label' : 'sph({})'.format(x['label']),
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'meta' : {
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'Unit' : x['meta']['Unit'],
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'Description' : 'Spherical component of tensor {} ({})'.format(x['label'],x['meta']['Description']),
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'Creator' : 'dadf5.py:add_spherical vXXXXX'
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}
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}
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requested = [{'label':x,'arg':'x'}]
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self.__add_generic_pointwise(spherical,requested)
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def add_deviator(self,x):
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"""Adds the deviator of a tensor."""
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def deviator(x):
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d = x['data']
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if not np.all(np.array(d.shape[1:]) == np.array([3,3])):
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raise ValueError
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return {
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'data' : d - np.einsum('ijk,i->ijk',np.broadcast_to(np.eye(3),[d.shape[0],3,3]),np.trace(d,axis1=1,axis2=2)/3.0),
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'label' : 'dev({})'.format(x['label']),
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'meta' : {
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'Unit' : x['meta']['Unit'],
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'Description' : 'Deviator of tensor {} ({})'.format(x['label'],x['meta']['Description']),
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'Creator' : 'dadf5.py:add_deviator vXXXXX'
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}
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}
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requested = [{'label':x,'arg':'x'}]
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self.__add_generic_pointwise(deviator,requested)
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def add_strain_tensor(self,t,ord,defgrad='F'): #ToDo: Use t and ord
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"""Adds the a strain tensor."""
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def strain_tensor(defgrad,t,ord):
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(U,S,Vh) = np.linalg.svd(defgrad['data']) # singular value decomposition
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R_inv = np.einsum('ikj',np.matmul(U,Vh)) # inverse rotation of polar decomposition
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U = np.matmul(R_inv,defgrad['data']) # F = RU
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(D,V) = np.linalg.eigh((U+np.einsum('ikj',U))*.5) # eigen decomposition (of symmetric(ed) matrix)
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neg = np.where(D < 0.0) # find negative eigenvalues ...
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D[neg[0],neg[1]] = D[neg[0],neg[1]]* -1 # ... flip value ...
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V[neg[0],:,neg[1]] = V[neg[0],:,neg[1]]* -1 # ... and vector
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d = np.log(D)
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a = np.matmul(V,np.einsum('ij,ikj->ijk',d,V))
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return {
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'data' : a,
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'label' : 'ln(V)({})'.format(defgrad['label']),
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'meta' : {
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'Unit' : defgrad['meta']['Unit'],
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'Description' : 'Strain tensor ln(V){} ({})'.format(defgrad['label'],defgrad['meta']['Description']),
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'Creator' : 'dadf5.py:add_deviator vXXXXX'
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}
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}
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requested = [{'label':defgrad,'arg':'defgrad'}]
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self.__add_generic_pointwise(strain_tensor,requested,{'t':t,'ord':ord})
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def __add_generic_pointwise(self,func,datasets_requested,extra_args={}):
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"""
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General function to add pointwise data.
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Parameters
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----------
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func : function
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datasets_requested : list of dictionaries
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extra_args : dictitionary
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"""
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def job(args):
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"""Call function with input data + extra arguments, returns results + group."""
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args['results'].put({**args['func'](**args['in']),'group':args['group']})
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N_threads = 1 # ToDo: should be a parameter
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results = Queue(N_threads)
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pool = util.ThreadPool(N_threads)
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N_added = N_threads + 1
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todo = []
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# ToDo: It would be more memory efficient to read only from file when required, i.e. do to it in pool.add_task
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for group in self.get_groups([d['label'] for d in datasets_requested]):
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with h5py.File(self.filename,'r') as f:
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datasets_in = {}
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for d in datasets_requested:
|
|
loc = f[group+'/'+d['label']]
|
|
data = loc[()]
|
|
meta = {k:loc.attrs[k] for k in loc.attrs.keys()}
|
|
datasets_in[d['arg']] = {'data': data, 'meta' : meta, 'label' : d['label']}
|
|
|
|
todo.append({'in':{**datasets_in,**extra_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.filename,'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]
|
|
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()
|