implemented ideas from discussion with Philip
group matching unified and with wildcard support time step handling should become more convenient (WIP) add_norm can not compute abs of scalar, added function for that general polishing here and there
This commit is contained in:
Martin Diehl
parent
c13db4b3ca
commit
b85ac11c49
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@ -24,9 +24,9 @@ parser.add_argument('filenames', nargs='+',
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parser.add_argument('-d','--dir', dest='dir',default='postProc',metavar='string',
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help='name of subdirectory to hold output')
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parser.add_argument('--mat', nargs='+',
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help='labels for materialpoint/homogenization',dest='mat')
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help='labels for materialpoint',dest='mat')
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parser.add_argument('--con', nargs='+',
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help='labels for constituent/crystallite/constitutive',dest='con')
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help='labels for constituent',dest='con')
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options = parser.parse_args()
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@ -67,11 +67,9 @@ for filename in options.filenames:
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x = results.get_dataset_location(label)
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if len(x) == 0:
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continue
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label = x[0].split('/')[-1]
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array = results.read_dataset(x,0)
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d = int(np.product(np.shape(array)[1:]))
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array = np.reshape(array,[np.product(results.grid),d])
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data = np.concatenate((data,array),1)
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data = np.concatenate((data,np.reshape(array,[np.product(results.grid),d])),1)
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if d>1:
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header+= ''.join([' {}_{}'.format(j+1,label) for j in range(d)])
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@ -84,11 +82,9 @@ for filename in options.filenames:
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x = results.get_dataset_location(label)
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if len(x) == 0:
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continue
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label = x[0].split('/')[-1]
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array = results.read_dataset(x,0)
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d = int(np.product(np.shape(array)[1:]))
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array = np.reshape(array,[np.product(results.grid),d])
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data = np.concatenate((data,array),1)
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data = np.concatenate((data,np.reshape(array,[np.product(results.grid),d])),1)
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if d>1:
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header+= ''.join([' {}_{}'.format(j+1,label) for j in range(d)])
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@ -1,5 +1,6 @@
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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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@ -37,10 +38,12 @@ class DADF5():
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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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self.time_information = [{'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.increments = self.time_information.copy() # unify later
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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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@ -99,6 +102,23 @@ class DADF5():
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self.__visible_set(a,t,a)
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def increment_set_by_time(self,start,end):
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for t in self.time_information:
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if start<= t['time']< end:
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print(t)
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def increment_set_by_position(self,start,end):
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for t in self.time_information[start:end]:
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print(t)
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def increment_set(self,start,end):
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for t in self.time_information:
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if start<= t['inc']< end:
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print(t)
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def constituent_output_iter(self):
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return self.__visible_iter('c_output_types')
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@ -167,37 +187,58 @@ class DADF5():
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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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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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l : list of str
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Names of datasets that need to be located in the group.
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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','sigma']
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datasets = ['*'] does not matche a group with ['F','P','sigma']
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datasets = ['*','*'] does not matche 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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if isinstance(datasets,str):
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s = [datasets]
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else:
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s = datasets
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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.visible['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.visible['constituents']:
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for t in self.visible['c_output_types']:
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groups.append('/'.join([group_inc,'constituent',c,t]))
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for m in self.visible['materialpoints']:
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for t in self.visible['m_output_types']:
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groups.append('/'.join([group_inc,'materialpoint',m,t]))
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for c in self.constituent_iter():
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for t in self.constituent_output_iter():
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group = '/'.join([group_inc,'constituent',c,t])
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if datasets 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 datasets] for e in e_]
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if len(set(match)) == len(s) : groups.append(group)
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for m in self.materialpoint_iter():
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for t in self.materialpoint_output_iter():
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group = '/'.join([group_inc,'materialpoint',m,t])
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if datasets 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 datasets] for e in e_]
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if len(set(match)) == len(s) : groups.append(group)
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return groups
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@ -316,14 +357,17 @@ class DADF5():
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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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def Mises(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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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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elif x['meta']['Unit'] == '-':
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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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ValueError
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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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@ -331,35 +375,29 @@ class DADF5():
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return {
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'data' : np.sqrt(np.einsum('ijk->i',dev**2)*factor),
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'label' : 'Mises({})'.format(x['label']),
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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 stress of {} ({})'.format(x['label'],x['meta']['Description']),
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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(deviator,requested)
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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 or magnitude of a scalar.
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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) == 1:
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axis = 0
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t = 'scalar'
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if o is None: o = 2
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elif len(x['data'].shape) == 2:
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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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@ -372,7 +410,7 @@ class DADF5():
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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'],ord),
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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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@ -385,6 +423,25 @@ class DADF5():
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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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@ -516,7 +573,7 @@ class DADF5():
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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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for group in self.groups_with_datasets([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:
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