577 lines
21 KiB
Python
577 lines
21 KiB
Python
from queue import Queue
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import re
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import glob
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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 = [i for i in f.keys() if r.match(i)]
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self.times = [round(f[i].attrs['time/s'],12) for i in self.increments]
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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.con_physics = []
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for c in self.constituents:
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self.con_physics += f['inc00000/constituent/{}'.format(c)].keys()
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self.con_physics = list(set(self.con_physics)) # make unique
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self.mat_physics = []
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for m in self.materialpoints:
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self.mat_physics += f['inc00000/materialpoint/{}'.format(m)].keys()
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self.mat_physics = list(set(self.mat_physics)) # make unique
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self.visible= {'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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'con_physics': self.con_physics,
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'mat_physics': self.mat_physics}
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self.filename = filename
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def __manage_visible(self,datasets,what,action):
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"""Manages the visibility of the groups."""
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# allow True/False and string arguments
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if datasets is True:
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datasets = ['*']
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elif datasets is False:
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datasets = []
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choice = [datasets] if isinstance(datasets,str) else datasets
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valid = [e for e_ in [glob.fnmatch.filter(getattr(self,what) ,s) for s in choice] for e in e_]
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existing = set(self.visible[what])
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if action == 'set':
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self.visible[what] = valid
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elif action == 'add':
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self.visible[what] = list(existing.union(valid))
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elif action == 'del':
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self.visible[what] = list(existing.difference_update(valid))
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def __time_to_inc(self,start,end):
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selected = []
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for i,time in enumerate(self.times):
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if start <= time < end:
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selected.append(self.increments[i])
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return selected
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def set_by_time(self,start,end):
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self.__manage_visible(self.__time_to_inc(start,end),'increments','set')
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def add_by_time(self,start,end):
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self.__manage_visible(self.__time_to_inc(start,end),'increments','add')
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def del_by_time(self,start,end):
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self.__manage_visible(self.__time_to_inc(start,end),'increments','del')
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def iter_visible(self,what):
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"""Iterates over visible items by setting each one visible."""
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datasets = self.visible[what]
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last_datasets = datasets.copy()
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for dataset in datasets:
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if last_datasets != self.visible[what]:
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self.__manage_visible(datasets,what,'set')
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raise Exception
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self.__manage_visible(dataset,what,'set')
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last_datasets = self.visible[what]
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yield dataset
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self.__manage_visible(datasets,what,'set')
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def set_visible(self,what,datasets):
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self.__manage_visible(datasets,what,'set')
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def add_visible(self,what,datasets):
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self.__manage_visible(datasets,what,'add')
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def del_visible(self,what,datasets):
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self.__manage_visible(datasets,what,'del')
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def groups_with_datasets(self,datasets):
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"""
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Get groups that contain all requested datasets.
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Only groups within inc?????/constituent/*_*/* inc?????/materialpoint/*_*/*
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are considered as they contain the data.
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Single strings will be treated as list with one entry.
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Wild card matching is allowed, but the number of arguments need to fit.
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Parameters
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----------
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datasets : iterable or str or boolean
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Examples
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--------
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datasets = False matches no group
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datasets = True matches all groups
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datasets = ['F','P'] matches a group with ['F','P','sigma']
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datasets = ['*','P'] matches a group with ['F','P']
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datasets = ['*'] does not match a group with ['F','P','sigma']
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datasets = ['*','*'] does not match a group with ['F','P','sigma']
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datasets = ['*','*','*'] matches a group with ['F','P','sigma']
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"""
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if datasets is False: return []
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sets = [datasets] if isinstance(datasets,str) else datasets
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groups = []
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with h5py.File(self.filename,'r') as f:
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for i in self.iter_visible('increments'): #ToDo: Merge path only once at the end '/'.join(listE)
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for c in self.iter_visible('constituents'):
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for p in self.iter_visible('con_physics'):
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group = '/'.join([i,'constituent',c,p])
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if sets is True:
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groups.append(group)
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else:
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match = [e for e_ in [glob.fnmatch.filter(f[group].keys(),s) for s in sets] for e in e_]
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if len(set(match)) == len(sets) : groups.append(group)
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for m in self.iter_visible('materialpoints'):
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for p in self.iter_visible('mat_physics'):
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group = '/'.join([i,'materialpoint',m,p])
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if sets is True:
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groups.append(group)
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else:
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match = [e for e_ in [glob.fnmatch.filter(f[group].keys(),s) for s in sets] for e in e_]
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if len(set(match)) == len(sets) : groups.append(group)
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return groups
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def list_data(self):
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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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i = 'inc{:05}'.format(0)
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for c in self.iter_visible('constituents'):
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print('{}'.format(c))
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for p in self.iter_visible('con_physics'):
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print(' {}'.format(p))
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try:
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k = '/'.join([i,'constituent',c,p])
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for d in f[k].keys():
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print(' {} ({})'.format(d,f[k+'/'+d].attrs['Description'].decode()))
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except KeyError:
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pass
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for m in self.iter_visible('materialpoints'):
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print('{}'.format(m))
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for p in self.iter_visible('mat_physics'):
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print(' {}'.format(p))
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try:
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k = '/'.join([i,'materialpoint',m,p])
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for d in f[k].keys():
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print(' {} ({})'.format(d,f[k+'/'+d].attrs['Description'].decode()))
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except KeyError:
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pass
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def get_dataset_location(self,label):
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"""Returns the location of all active datasets with given label."""
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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.iter_visible('increments'):
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for c in self.iter_visible('constituents'):
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for p in self.iter_visible('con_physics'):
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try:
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k = '/'.join([i,'constituent',c,p,label])
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f[k]
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path.append(k)
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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.iter_visible('materialpoints'):
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for p in self.iter_visible('mat_physics'):
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try:
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k = '/'.join([i,'materialpoint',m,p,label])
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f[k]
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path.append(k)
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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'],F['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 Mises(x):
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if x['meta']['Unit'] == b'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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t = 'stress'
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elif x['meta']['Unit'] == b'1':
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factor = 2.0/3.0
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t = 'strain'
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else:
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print(x['meta']['Unit'])
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raise 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' : '{}_vM'.format(x['label']),
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'meta' : {
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'Unit' : x['meta']['Unit'],
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'Description' : 'Mises equivalent {} of {} ({})'.format(t,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(Mises,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.
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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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o = ord
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if len(x['data'].shape) == 2:
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axis = 1
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t = 'vector'
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if o is None: o = 2
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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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if o is None: o = 'fro'
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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=o,axis=axis,keepdims=True),
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'label' : '|{}|_{}'.format(x['label'],o),
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'meta' : {
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'Unit' : x['meta']['Unit'],
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'Description' : '{}-Norm of {} {} ({})'.format(ord,t,x['label'],x['meta']['Description']),
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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_absolute(self,x):
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"""Adds absolute value."""
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def absolute(x):
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return {
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'data' : np.abs(x['data']),
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'label' : '|{}|'.format(x['label']),
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'meta' : {
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'Unit' : x['meta']['Unit'],
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'Description' : 'Absolute value of {} ({})'.format(x['label'],x['meta']['Description']),
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'Creator' : 'dadf5.py:add_abs 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(absolute,requested)
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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_calculation(self,formula,label,unit='n/a',description=None,vectorized=True):
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"""
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General formula.
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Works currently only for vectorized expressions
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"""
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if vectorized is not True:
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raise NotImplementedError
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def calculation(**kwargs):
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formula = kwargs['formula']
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for d in re.findall(r'#(.*?)#',formula):
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formula = re.sub('#{}#'.format(d),"kwargs['{}']['data']".format(d),formula)
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return {
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'data' : eval(formula),
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'label' : kwargs['label'],
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'meta' : {
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'Unit' : kwargs['unit'],
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'Description' : '{}'.format(kwargs['description']),
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'Creator' : 'dadf5.py:add_calculation vXXXXX'
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}
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}
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requested = [{'label':d,'arg':d} for d in re.findall(r'#(.*?)#',formula)] # datasets used in the formula
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pass_through = {'formula':formula,'label':label,'unit':unit,'description':description}
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self.__add_generic_pointwise(calculation,requested,pass_through)
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def add_strain_tensor(self,t,ord,defgrad='F'): #ToDo: Use t and ord
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"""
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Adds the a strain tensor.
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Albrecht Bertram: Elasticity and Plasticity of Large Deformations An Introduction (3rd Edition, 2012), p. 102.
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"""
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def strain_tensor(defgrad,t,ord):
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operator = {
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'V#ln': lambda V: np.log(V),
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'U#ln': lambda U: np.log(U),
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'V#Biot': lambda V: np.broadcast_to(np.ones(3),[V.shape[0],3]) - 1.0/V,
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'U#Biot': lambda U: U - np.broadcast_to(np.ones(3),[U.shape[0],3]),
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'V#Green':lambda V: np.broadcast_to(np.ones(3),[V.shape[0],3]) - 1.0/V**2,
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'U#Biot': lambda U: U**2 - np.broadcast_to(np.ones(3),[U.shape[0],3]),
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}
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|
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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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|
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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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|
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d = operator['V#ln'](D)
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a = np.matmul(V,np.einsum('ij,ikj->ijk',d,V))
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|
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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'],
|
|
'Description' : 'Strain tensor ln(V){} ({})'.format(defgrad['label'],defgrad['meta']['Description']),
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|
'Creator' : 'dadf5.py:add_deviator vXXXXX'
|
|
}
|
|
}
|
|
|
|
requested = [{'label':defgrad,'arg':'defgrad'}]
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|
|
|
self.__add_generic_pointwise(strain_tensor,requested,{'t':t,'ord':ord})
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|
|
|
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|
def __add_generic_pointwise(self,func,datasets_requested,extra_args={}):
|
|
"""
|
|
General function to add pointwise data.
|
|
|
|
Parameters
|
|
----------
|
|
func : function
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|
Function that calculates a new dataset from one or more datasets per HDF5 group.
|
|
datasets_requested : list of dictionaries
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|
Details of the datasets to be used: label (in HDF5 file) and arg (argument to which the data is parsed in func).
|
|
extra_args : dictionary, optional
|
|
Any extra arguments parsed to func.
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|
|
|
"""
|
|
def job(args):
|
|
"""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
|
|
|
|
results = Queue(N_threads)
|
|
pool = util.ThreadPool(N_threads)
|
|
N_added = N_threads + 1
|
|
|
|
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([d['label'] for d in datasets_requested]):
|
|
with h5py.File(self.filename,'r') as f:
|
|
datasets_in = {}
|
|
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()
|