rewrite pointwise add function
- all vectorized: Much faster - passing in all relevant information allows to do sanity checks and add useful meta data in HDF5 - improved readability
This commit is contained in:
Martin Diehl
parent
3db3e9e762
commit
6f008c5d5f
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@ -84,7 +84,7 @@ class DADF5():
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self.mode = mode
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self.mode = mode
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def get_candidates(self,l):
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def get_groups(self,l):
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"""
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"""
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Get groups that contain all requested datasets.
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Get groups that contain all requested datasets.
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@ -222,252 +222,224 @@ class DADF5():
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"""
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"""
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def Cauchy(F,P):
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def Cauchy(F,P):
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return np.einsum('i,ijk,ilk->ijl',1.0/np.linalg.det(F),F,P)
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return {
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'data' : np.einsum('i,ijk,ilk->ijl',1.0/np.linalg.det(F['data']),F['data'],P['data']),
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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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args = [{'label':F,'shape':[3,3],'unit':'-'},
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requested = [{'label':F,'arg':'F'},
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{'label':P,'shape':[3,3],'unit':'Pa'} ]
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{'label':P,'arg':'P'} ]
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result = {'label':'sigma',
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'unit':'Pa',
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'Description': 'Cauchy stress calculated from 1st Piola-Kirchhoff stress and deformation gradient'}
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self.add_generic_pointwise_vectorized(Cauchy,args,None,result)
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self.__add_generic_pointwise(Cauchy,requested)
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def add_Mises_stress(self,stress='sigma'):
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def add_Mises(self,x):
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"""Adds equivalent von Mises stress."""
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"""Adds the equivalent Mises stres of a tensor."""
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def Mises_stress(stress):
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def deviator(x):
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dev = stress - np.trace(stress)/3.0*np.eye(3)
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symdev = 0.5*(dev+dev.T)
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return np.sqrt(np.sum(symdev*symdev.T)*3.0/2.0)
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args = [{'label':stress,'shape':[3,3],'unit':'Pa'}]
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if x['meta']['Unit'] == 'Pa':
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result = {'label':'Mises({})'.format(stress),
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factor = 3.0/2.0
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'unit':'Pa',
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elif x['meta']['Unit'] == '-':
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'Description': 'Equivalent Mises stress'}
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factor = 2.0/3.0
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else:
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ValueError
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self.add_generic_pointwise(Mises_stress,args,result)
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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
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return {
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'data' : np.sqrt(np.einsum('ijk->i',dev_sym**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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def add_norm(self,x,ord=None):
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"""
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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 or magnitude of a scalar.
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See numpy.linalg.norm manual for details.
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Todo
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----
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The output unit should be the input unit.
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The ord parameter should be taken into account.
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The whole thing should be vectorized. This requires to parse optional arguments to func.
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"""
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"""
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args = [{'label':x,'shape':None,'unit':None}]
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def norm(x,ord):
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result = {'label':'norm_{}({})'.format(str(ord),x),
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'unit':'n/a',
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'Description': 'Norm of vector or tensor or magnitude of a scalar. See numpy.linalg.norm manual for details'}
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self.add_generic_pointwise(np.linalg.norm,args,result)
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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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def add_determinant(self,a):
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elif len(x['data'].shape) == 2:
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"""Adds the determinant of a tensor."""
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axis = 1
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# ToDo: The output unit should be the input unit
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t = 'vector'
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args = [{'label':a,'shape':[3,3],'unit':None}]
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elif len(x['data'].shape) == 3:
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result = {'label':'det({})'.format(a),
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axis = (1,2)
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'unit':'n/a',
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t = 'tensor'
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'Description': 'Determinant of a tensor'}
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self.add_generic_pointwise_vectorized(np.linalg.det,args,result)
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def add_spherical(self,a):
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"""Adds the spherical component of a tensor."""
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def spherical(m):
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return (m[0,0]+m[1,1]+m[2,2])/3.0
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# ToDo: The output unit should be the input unit
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args = [{'label':a,'shape':[3,3],'unit':None}]
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result = {'label':'sph({})'.format(a),
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'unit':'n/a',
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'Description': 'Spherical component of a tensor'}
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self.add_generic_pointwise(spherical,args,result)
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def add_deviator(self,a):
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"""Adds the deviator of a tensor."""
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def deviator(m):
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return m - np.eye(3)*(m[0,0]+m[1,1]+m[2,2])/3.0
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# ToDo: The output unit should be the input unit
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args = [{'label':a,'shape':[3,3],'unit':'Pa'}]
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result = {'label':'dev({})'.format(a),
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'unit':'n/a',
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'Description': 'Deviatoric component of a tensor'}
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self.add_generic_pointwise(deviator,args,result)
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def add_strain_tensors(self,defgrad='F'):
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"""Adds a strain definition."""
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def strain(defgrad):
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(U,S,Vh) = np.linalg.svd(defgrad) # singular value decomposition
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R_inv = np.dot(U,Vh).T # inverse rotation of polar decomposition
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U = np.dot(R_inv,defgrad) # F = RU
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U = np.where(abs(U) < 1e-12, 0, U) # kill nasty noisy data
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(D,V) = np.linalg.eig(U) # eigen decomposition (of symmetric matrix)
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neg = np.where(D < 0.0) # find negative eigenvalues ...
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D[neg] *= -1. # ... flip value ...
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V[:,neg] *= -1. # ... and vector
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for i,eigval in enumerate(D):
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if np.dot(V[:,i],V[:,(i+1)%3]) != 0.0: # check each vector for orthogonality
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V[:,(i+1)%3] = np.cross(V[:,(i+2)%3],V[:,i]) # correct next vector
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V[:,(i+1)%3] /= np.sqrt(np.dot(V[:,(i+1)%3],V[:,(i+1)%3].conj())) # and renormalize (hyperphobic?)
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d = np.log(D) # operate on eigenvalues of U o r V
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return np.dot(V,np.dot(np.diag(d),V.T)).real # build tensor back from eigenvalue/vector basis
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# ToDo: The output unit should be the input unit
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args = [{'label':defgrad,'shape':[3,3],'unit':None}]
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result = {'label':'strain({})'.format(defgrad),
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'unit':'-',
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'Description': 'strain (ln(V)) of a deformation gradient'}
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self.add_generic_pointwise(strain,args,result)
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def get_fitting(self,data):
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groups = []
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if type(data) is not list:
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print('mist')
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with h5py.File(self.filename,'r') as f:
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for g in self.get_candidates([l['label'] for l in data]):
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print(g)
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fits = True
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for d in data: # ToDo: check for unit
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if d['shape'] is not None:
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fits = fits and np.all(np.array(f[g+'/'+d['label']].shape[1:]) == np.array(d['shape']))
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if fits: groups.append(g)
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return groups
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def add_generic_pointwise(self,func,args,result):
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"""
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General function to add pointwise data.
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function 'func' first needs to have data arguments before other arguments
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Works for functions that are pointwise defined.
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"""
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groups = self.get_fitting(args)
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def job(args):
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out = args['out']
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datasets_in = args['dat']
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func = args['fun']
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for i in range(out.shape[0]):
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arg = tuple([d[i,] for d in datasets_in])
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out[i,] = func(*arg)
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args['results'].put({'out':out,'group':args['group']})
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Nthreads = 4 # ToDo: should be a parameter
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results = Queue(Nthreads+1)
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todo = []
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for g in groups:
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with h5py.File(self.filename,'r') as f:
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datasets_in = [f[g+'/'+u['label']][()] for u in args]
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# figure out dimension of results
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testArg = tuple([d[0,] for d in datasets_in]) # to call function with first point
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out = np.empty([datasets_in[0].shape[0]] + list(func(*testArg).shape)) # shape is Npoints x shape of the results for one point
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todo.append({'dat':datasets_in,'fun':func,'out':out,'group':g,'results':results})
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# Instantiate a thread pool with worker threads
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pool = util.ThreadPool(Nthreads)
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missingResults = len(todo)
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# Add the jobs in bulk to the thread pool. Alternatively you could use
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# `pool.add_task` to add single jobs. The code will block here, which
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# makes it possible to cancel the thread pool with an exception when
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# the currently running batch of workers is finished
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pool.map(job, todo[:Nthreads+1])
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i = 0
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while missingResults > 0:
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r=results.get() # noqa
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print(r['group'])
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with h5py.File(self.filename,'r+') as f:
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dataset_out = f[r['group']].create_dataset(result['label'],data=r['out'])
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dataset_out.attrs['Unit'] = result['unit']
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dataset_out.attrs['Description'] = result['Description']
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dataset_out.attrs['Creator'] = 'dadf5.py v{}'.format('n/a')
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missingResults-=1
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try:
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pool.add_task(job,todo[Nthreads+1+i])
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except IndexError:
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pass
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i+=1
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pool.wait_completion()
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def add_generic_pointwise_vectorized(self,func,args,args2=None,result=None):
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"""
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General function to add pointwise data.
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function 'func' first needs to have data arguments before other arguments
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Works for vectorized functions.
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"""
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groups = self.get_fitting(args)
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def job(args):
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"""
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A job. It has different args!
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"""
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print('args for job',args)
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out = args['out']
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datasets_in = args['dat']
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func = args['fun']
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# try:
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# out = func(*datasets_in,*args['fun_args'])
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# except:
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out = func(*datasets_in)
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args['results'].put({'out':out,'group':args['group']})
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Nthreads = 4 # ToDo: should be a parameter
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results = Queue(Nthreads+1)
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todo = []
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for g in groups:
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with h5py.File(self.filename,'r') as f:
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datasets_in = [f[g+'/'+u['label']][()] for u in args]
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if args2 is not None:
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todo.append({'dat':datasets_in,'fun':func,'group':g,'results':results,'func_args':args,'out':None})
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else:
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else:
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todo.append({'dat':datasets_in,'fun':func,'group':g,'results':results,'out':None})
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raise ValueError
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# Instantiate a thread pool with worker threads
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return {
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pool = util.ThreadPool(Nthreads)
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'data' : np.linalg.norm(x['data'],ord=ord,axis=axis,keepdims=True),
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missingResults = len(todo)
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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']),
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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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pool.map(job, todo[:Nthreads+1])
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def add_determinant(self,x):
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i = 0
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"""Adds the determinant component of a tensor."""
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while missingResults > 0:
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def determinant(x):
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r=results.get() # noqa
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print(r['group'])
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return {
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with h5py.File(self.filename,'r+') as f:
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'data' : np.linalg.det(x['data']),
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dataset_out = f[r['group']].create_dataset(result['label'],data=r['out'])
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'label' : 'det({})'.format(x['label']),
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dataset_out.attrs['Unit'] = result['unit']
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'meta' : {
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dataset_out.attrs['Description'] = result['Description']
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'Unit' : x['meta']['Unit'],
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dataset_out.attrs['Creator'] = 'dadf5.py v{}'.format('n/a')
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'Description' : 'Determinant of tensor {} ({})'.format(x['label'],x['meta']['Description']),
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missingResults-=1
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'Creator' : 'dadf5.py:add_determinant vXXXXX'
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try:
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}
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pool.add_task(job,todo[Nthreads+1+i])
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}
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except IndexError:
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pass
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requested = [{'label':x,'arg':'x'}]
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i+=1
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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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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'):
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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('ijk->ikj',np.matmul(U,Vh)) # inverse rotation of polar decomposition
|
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U = np.matmul(R_inv,defgrad['data']) # F = RU
|
||||||
|
(D,V) = np.linalg.eigh((U+np.einsum('ikj',U))*.5) # eigen decomposition (of symmetric(ed) matrix)
|
||||||
|
|
||||||
|
neg = np.where(D < 0.0) # find negative eigenvalues ...
|
||||||
|
D[neg[0],neg[1]] = D[neg[0],neg[1]]* -1 # ... flip value ...
|
||||||
|
V[neg[0],:,neg[1]] = V[neg[0],:,neg[1]]* -1 # ... and vector
|
||||||
|
|
||||||
|
d = np.log(D)
|
||||||
|
a = np.matmul(V,np.einsum('ij,ikj->ijk',d,V)) # this is wrong ...
|
||||||
|
for j in range(V.shape[0]): # but this is slow ...
|
||||||
|
a[j,:,:] = np.dot(V[j,:,:],np.dot(np.diag(d[j,:]),V[j,:,:].T))
|
||||||
|
print(np.max(a))
|
||||||
|
|
||||||
|
return {
|
||||||
|
'data' : a,
|
||||||
|
'label' : 'lnV({})'.format(defgrad['label']),
|
||||||
|
'meta' : {
|
||||||
|
'Unit' : defgrad['meta']['Unit'],
|
||||||
|
'Description' : 'Strain tensor {} ({})'.format(defgrad['label'],defgrad['meta']['Description']),
|
||||||
|
'Creator' : 'dadf5.py:add_deviator vXXXXX'
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
requested = [{'label':defgrad,'arg':'defgrad'}]
|
||||||
|
|
||||||
|
self.__add_generic_pointwise(strain_tensor,requested,{'t':t,'ord':ord})
|
||||||
|
|
||||||
|
|
||||||
|
def __add_generic_pointwise(self,func,datasets_requested,extra_args={}):
|
||||||
|
"""
|
||||||
|
General function to add pointwise data.
|
||||||
|
"""
|
||||||
|
|
||||||
|
def job(args):
|
||||||
|
"""
|
||||||
|
Call function with input data + extra arguments, returns results + group.
|
||||||
|
"""
|
||||||
|
args['results'].put({**args['func'](**args['in']),'group':args['group']})
|
||||||
|
|
||||||
|
|
||||||
|
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.get_groups([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,self.mode) 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()
|
pool.wait_completion()
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue