functionality to add field data on regular grids
allows to add curl, divergence, and gradient to results from the grid solver
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Martin Diehl
parent
e431d89957
commit
46dc6b4dab
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@ -110,6 +110,10 @@ class Result:
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self.size = f['geometry'].attrs['size']
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self.origin = f['geometry'].attrs['origin']
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self.add_divergence = self._add_divergence
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self.add_curl = self._add_curl
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self.add_gradient = self._add_gradient
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r=re.compile('increment_[0-9]+')
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self.increments = sorted([i for i in f.keys() if r.match(i)],key=util.natural_sort)
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self.times = [round(f[i].attrs['t/s'],12) for i in self.increments]
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@ -1127,7 +1131,7 @@ class Result:
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'meta': {
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'unit': F['meta']['unit'],
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'description': f"{'left' if t.upper() == 'V' else 'right'} stretch tensor "\
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+f"of {F['label']} ({F['meta']['description']})",
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+f"of {F['label']} ({F['meta']['description']})", # noqa
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'creator': 'add_stretch_tensor'
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}
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}
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@ -1147,6 +1151,132 @@ class Result:
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self._add_generic_pointwise(self._add_stretch_tensor,{'F':F},{'t':t})
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@staticmethod
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def _add_curl_(f,size):
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return {
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'data': grid_filters.curl(size,f['data']),
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'label': f"curl({f['label']})",
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'meta': {
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'unit': f['meta']['unit']+'/m',
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'description': f"curl of {f['label']} ({f['meta']['description']})",
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'creator': 'add_curl'
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}
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}
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def _add_curl(self,f):
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"""
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Add curl of a field.
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Parameters
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----------
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f : str
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Name of vector or tensor field dataset.
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"""
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self._add_generic_grid(self._add_curl_,{'f':f},{'size':self.size})
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@staticmethod
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def _add_divergence_(f,size):
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return {
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'data': grid_filters.divergence(size,f['data']),
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'label': f"divergence({f['label']})",
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'meta': {
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'unit': f['meta']['unit']+'/m',
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'description': f"divergence of {f['label']} ({f['meta']['description']})",
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'creator': 'add_divergence'
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}
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}
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def _add_divergence(self,f):
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"""
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Add divergence of a field.
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Parameters
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----------
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f : str
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Name of vector or tensor field dataset.
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"""
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self._add_generic_grid(self._add_divergence_,{'f':f},{'size':self.size})
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@staticmethod
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def _add_gradient_(f,size):
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return {
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'data': grid_filters.gradient(size,f['data'] if len(f['data'].shape) == 4 else \
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f['data'].reshape(f['data'].shape+(1,))),
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'label': f"gradient({f['label']})",
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'meta': {
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'unit': f['meta']['unit']+'/m',
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'description': f"gradient of {f['label']} ({f['meta']['description']})",
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'creator': 'add_gradient'
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}
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}
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def _add_gradient(self,f):
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"""
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Add gradient of a field.
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Parameters
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----------
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f : str
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Name of scalar or vector field dataset.
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"""
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self._add_generic_grid(self._add_gradient_,{'f':f},{'size':self.size})
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def _add_generic_grid(self,func,datasets,args={},constituents=None):
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"""
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General function to add data on a regular grid.
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Parameters
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----------
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func : function
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Callback function that calculates a new dataset from one or
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more datasets per HDF5 group.
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datasets : dictionary
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Details of the datasets to be used:
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{arg (name to which the data is passed in func): label (in HDF5 file)}.
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args : dictionary, optional
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Arguments parsed to func.
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"""
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if len(datasets) != 1 or self.N_constituents !=1:
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raise NotImplementedError
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at_cell_ph,in_data_ph,at_cell_ho,in_data_ho = self._mappings()
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with h5py.File(self.fname, 'a') as f:
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for increment in self.place(datasets.values(),False).items():
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for ty in increment[1].items():
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for field in ty[1].items():
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d = list(field[1].values())[0]
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if np.any(d.mask): continue
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dataset = {'f':{'data':np.reshape(d.data,tuple(self.cells)+d.data.shape[1:]),
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'label':list(datasets.values())[0],
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'meta':d.data.dtype.metadata}}
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r = func(**dataset,**args)
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result = r['data'].reshape((-1,)+r['data'].shape[3:])
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for x in self.visible[ty[0]+'s']:
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if ty[0] == 'phase':
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result1 = result[at_cell_ph[0][x]]
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if ty[0] == 'homogenization':
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result1 = result[at_cell_ho[x]]
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path = '/'.join(['/',increment[0],ty[0],x,field[0]])
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dataset = f[path].create_dataset(r['label'],data=result1)
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now = datetime.datetime.now().astimezone()
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dataset.attrs['created'] = now.strftime('%Y-%m-%d %H:%M:%S%z') if h5py3 else \
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now.strftime('%Y-%m-%d %H:%M:%S%z').encode()
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for l,v in r['meta'].items():
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dataset.attrs[l.lower()]=v if h5py3 else v.encode()
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creator = dataset.attrs['creator'] if h5py3 else \
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dataset.attrs['creator'].decode()
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dataset.attrs['creator'] = f'damask.Result.{creator} v{damask.version}' if h5py3 else \
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f'damask.Result.{creator} v{damask.version}'.encode()
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def _job_pointwise(self,group,func,datasets,args,lock):
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"""Execute job for _add_generic_pointwise."""
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try:
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@ -1163,7 +1293,7 @@ class Result:
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return [group,r]
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except Exception as err:
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print(f'Error during calculation: {err}.')
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return None
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return [None,None]
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def _add_generic_pointwise(self,func,datasets,args={}):
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"""
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@ -269,6 +269,38 @@ class TestResult:
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with pytest.raises(TypeError):
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default.add_calculation('#invalid#*2')
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@pytest.mark.parametrize('shape',['vector','tensor'])
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def test_add_curl(self,default,shape):
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if shape == 'vector': default.add_calculation('#F#[:,:,0]','x','1','just a vector')
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if shape == 'tensor': default.add_calculation('#F#[:,:,:]','x','1','just a tensor')
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x = default.place('x')
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default.add_curl('x')
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in_file = default.place('curl(x)')
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in_memory = grid_filters.curl(default.size,x.reshape(tuple(default.cells)+x.shape[1:])).reshape(in_file.shape)
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assert (in_file==in_memory).all()
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@pytest.mark.parametrize('shape',['vector','tensor'])
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def test_add_divergence(self,default,shape):
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if shape == 'vector': default.add_calculation('#F#[:,:,0]','x','1','just a vector')
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if shape == 'tensor': default.add_calculation('#F#[:,:,:]','x','1','just a tensor')
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x = default.place('x')
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default.add_divergence('x')
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in_file = default.place('divergence(x)')
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in_memory = grid_filters.divergence(default.size,x.reshape(tuple(default.cells)+x.shape[1:])).reshape(in_file.shape)
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assert (in_file==in_memory).all()
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@pytest.mark.parametrize('shape',['scalar','pseudo_scalar','vector'])
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def test_add_gradient(self,default,shape):
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if shape == 'pseudo_scalar': default.add_calculation('#F#[:,0,0:1]','x','1','a pseudo scalar')
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if shape == 'scalar': default.add_calculation('#F#[:,0,0]','x','1','just a scalar')
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if shape == 'vector': default.add_calculation('#F#[:,:,1]','x','1','just a vector')
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x = default.place('x').reshape((np.product(default.cells),-1))
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default.add_gradient('x')
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in_file = default.place('gradient(x)')
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in_memory = grid_filters.gradient(default.size,x.reshape(tuple(default.cells)+x.shape[1:])).reshape(in_file.shape)
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assert (in_file==in_memory).all()
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@pytest.mark.parametrize('overwrite',['off','on'])
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def test_add_overwrite(self,default,overwrite):
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last = default.view('increments',-1)
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