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updated option --label for addCurl/Div/Grad

This commit is contained in:
Philip Eisenlohr 2018-01-29 21:27:05 -05:00
parent 1d71a52133
commit 710970d728
5 changed files with 81 additions and 72 deletions
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2
PRIVATE

@ -1 +1 @@
Subproject commit 1c1e8008489d81773a13a247604144a8d7ee3723
Subproject commit bdbf2da71cd9e0825d17f673ec2fbabc2c8027f8

2
lib/damask/test/test.py
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@ -109,7 +109,7 @@ class Test():
except Exception as e:
logging.critical('exception during variant execution: "{}"'.format(str(e)))
return variant+1 # return culprit
return variant+1 # return culprit
return 0
def feasible(self):

2
processing/post/addCurl.py
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@ -66,7 +66,7 @@ parser.add_option('-p','--pos','--periodiccellcenter',
dest = 'pos',
type = 'string', metavar = 'string',
help = 'label of coordinates [%default]')
parser.add_option('-d','--data',
parser.add_option('-l','--label',
dest = 'data',
action = 'extend', metavar = '<string LIST>',
help = 'label(s) of field values')

145
processing/post/addDivergence.py
View File

@ -9,36 +9,43 @@ import damask
scriptName = os.path.splitext(os.path.basename(__file__))[0]
scriptID = ' '.join([scriptName,damask.version])
def merge_dicts(*dict_args):
"""Given any number of dicts, shallow copy and merge into a new dict, with precedence going to key value pairs in latter dicts."""
result = {}
for dictionary in dict_args:
result.update(dictionary)
return result
def divFFT(geomdim,field):
shapeFFT = np.array(np.shape(field))[0:3]
grid = np.array(np.shape(field)[2::-1])
N = grid.prod() # field size
n = np.array(np.shape(field)[3:]).prod() # data size
"""Calculate divergence of a vector or tensor field by transforming into Fourier space."""
shapeFFT = np.array(np.shape(field))[0:3]
grid = np.array(np.shape(field)[2::-1])
N = grid.prod() # field size
n = np.array(np.shape(field)[3:]).prod() # data size
if n == 3: dataType = 'vector'
elif n == 9: dataType = 'tensor'
field_fourier = np.fft.rfftn(field,axes=(0,1,2),s=shapeFFT)
div_fourier = np.empty(field_fourier.shape[0:len(np.shape(field))-1],'c16')
field_fourier = np.fft.rfftn(field,axes=(0,1,2),s=shapeFFT)
div_fourier = np.empty(field_fourier.shape[0:len(np.shape(field))-1],'c16')
# differentiation in Fourier space
TWOPIIMG = 2.0j*math.pi
einsums = {
3:'ijkl,ijkl->ijk', # vector, 3 -> 1
9:'ijkm,ijklm->ijkl', # tensor, 3x3 -> 3
}
k_sk = np.where(np.arange(grid[2])>grid[2]//2,np.arange(grid[2])-grid[2],np.arange(grid[2]))/geomdim[0]
if grid[2]%2 == 0: k_sk[grid[2]//2] = 0 # Nyquist freq=0 for even grid (Johnson, MIT, 2011)
# differentiation in Fourier space
TWOPIIMG = 2.0j*math.pi
k_sk = np.where(np.arange(grid[2])>grid[2]//2,np.arange(grid[2])-grid[2],np.arange(grid[2]))/geomdim[0]
if grid[2]%2 == 0: k_sk[grid[2]//2] = 0 # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011)
k_sj = np.where(np.arange(grid[1])>grid[1]//2,np.arange(grid[1])-grid[1],np.arange(grid[1]))/geomdim[1]
if grid[1]%2 == 0: k_sj[grid[1]//2] = 0 # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011)
k_sj = np.where(np.arange(grid[1])>grid[1]//2,np.arange(grid[1])-grid[1],np.arange(grid[1]))/geomdim[1]
if grid[1]%2 == 0: k_sj[grid[1]//2] = 0 # Nyquist freq=0 for even grid (Johnson, MIT, 2011)
k_si = np.arange(grid[0]//2+1)/geomdim[2]
kk, kj, ki = np.meshgrid(k_sk,k_sj,k_si,indexing = 'ij')
k_s = np.concatenate((ki[:,:,:,None],kj[:,:,:,None],kk[:,:,:,None]),axis = 3).astype('c16')
if dataType == 'tensor': # tensor, 3x3 -> 3
div_fourier = np.einsum('ijklm,ijkm->ijkl',field_fourier,k_s)*TWOPIIMG
elif dataType == 'vector': # vector, 3 -> 1
div_fourier = np.einsum('ijkl,ijkl->ijk',field_fourier,k_s)*TWOPIIMG
return np.fft.irfftn(div_fourier,axes=(0,1,2),s=shapeFFT).reshape([N,n/3])
k_si = np.arange(grid[0]//2+1)/geomdim[2]
kk, kj, ki = np.meshgrid(k_sk,k_sj,k_si,indexing = 'ij')
k_s = np.concatenate((ki[:,:,:,None],kj[:,:,:,None],kk[:,:,:,None]),axis = 3).astype('c16')
div_fourier = np.einsum(einsums[n],k_s,field_fourier)*TWOPIIMG
return np.fft.irfftn(div_fourier,axes=(0,1,2),s=shapeFFT).reshape([N,n/3])
# --------------------------------------------------------------------
@ -46,32 +53,38 @@ def divFFT(geomdim,field):
# --------------------------------------------------------------------
parser = OptionParser(option_class=damask.extendableOption, usage='%prog option(s) [ASCIItable(s)]', description = """
Add column(s) containing divergence of requested column(s).
Operates on periodic ordered three-dimensional data sets.
Deals with both vector- and tensor-valued fields.
Add column(s) containing curl of requested column(s).
Operates on periodic ordered three-dimensional data sets
of vector and tensor fields.
""", version = scriptID)
parser.add_option('-p','--pos','--periodiccellcenter',
dest = 'pos',
type = 'string', metavar = 'string',
help = 'label of coordinates [%default]')
parser.add_option('-v','--vector',
dest = 'vector',
parser.add_option('-l','--label',
dest = 'data',
action = 'extend', metavar = '<string LIST>',
help = 'label(s) of vector field values')
parser.add_option('-t','--tensor',
dest = 'tensor',
action = 'extend', metavar = '<string LIST>',
help = 'label(s) of tensor field values')
help = 'label(s) of field values')
parser.set_defaults(pos = 'pos',
)
(options,filenames) = parser.parse_args()
if options.vector is None and options.tensor is None:
parser.error('no data column specified.')
if options.data is None: parser.error('no data column specified.')
# --- define possible data types -------------------------------------------------------------------
datatypes = {
3: {'name': 'vector',
'shape': [3],
},
9: {'name': 'tensor',
'shape': [3,3],
},
}
# --- loop over input files ------------------------------------------------------------------------
@ -82,30 +95,27 @@ for name in filenames:
except: continue
damask.util.report(scriptName,name)
# ------------------------------------------ read header ------------------------------------------
# --- interpret header ----------------------------------------------------------------------------
table.head_read()
# ------------------------------------------ sanity checks ----------------------------------------
items = {
'tensor': {'dim': 9, 'shape': [3,3], 'labels':options.tensor, 'active':[], 'column': []},
'vector': {'dim': 3, 'shape': [3], 'labels':options.vector, 'active':[], 'column': []},
}
errors = []
remarks = []
column = {}
if table.label_dimension(options.pos) != 3: errors.append('coordinates {} are not a vector.'.format(options.pos))
else: colCoord = table.label_index(options.pos)
errors = []
active = []
for type, data in items.iteritems():
for what in (data['labels'] if data['labels'] is not None else []):
dim = table.label_dimension(what)
if dim != data['dim']: remarks.append('column {} is not a {}.'.format(what,type))
else:
items[type]['active'].append(what)
items[type]['column'].append(table.label_index(what))
coordDim = table.label_dimension(options.pos)
if coordDim != 3:
errors.append('coordinates "{}" must be three-dimensional.'.format(options.pos))
else: coordCol = table.label_index(options.pos)
for me in options.data:
dim = table.label_dimension(me)
if dim in datatypes:
active.append(merge_dicts({'label':me},datatypes[dim]))
remarks.append('differentiating {} "{}"...'.format(datatypes[dim]['name'],me))
else:
remarks.append('skipping "{}" of dimension {}...'.format(me,dim) if dim != -1 else \
'"{}" not found...'.format(me) )
if remarks != []: damask.util.croak(remarks)
if errors != []:
@ -116,17 +126,17 @@ for name in filenames:
# ------------------------------------------ assemble header --------------------------------------
table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
for type, data in items.iteritems():
for label in data['active']:
table.labels_append(['divFFT({})'.format(label) if type == 'vector' else
'{}_divFFT({})'.format(i+1,label) for i in range(data['dim']//3)]) # extend ASCII header with new labels
for data in active:
table.labels_append(['divFFT({})'.format(data['label']) if data['shape'] == [3] \
else '{}_divFFT({})'.format(i+1,data['label'])
for i in range(np.prod(np.array(data['shape']))//3)]) # extend ASCII header with new labels
table.head_write()
# --------------- figure out size and grid ---------------------------------------------------------
table.data_readArray()
coords = [np.unique(table.data[:,colCoord+i]) for i in range(3)]
coords = [np.unique(table.data[:,coordCol+i]) for i in range(3)]
mincorner = np.array(map(min,coords))
maxcorner = np.array(map(max,coords))
grid = np.array(map(len,coords),'i')
@ -136,12 +146,11 @@ for name in filenames:
# ------------------------------------------ process value field -----------------------------------
stack = [table.data]
for type, data in items.iteritems():
for i,label in enumerate(data['active']):
# we need to reverse order here, because x is fastest,ie rightmost, but leftmost in our x,y,z notation
stack.append(divFFT(size[::-1],
table.data[:,data['column'][i]:data['column'][i]+data['dim']].
reshape(grid[::-1].tolist()+data['shape'])))
for data in active:
# we need to reverse order here, because x is fastest,ie rightmost, but leftmost in our x,y,z notation
stack.append(divFFT(size[::-1],
table.data[:,table.label_indexrange(data['label'])].
reshape(grid[::-1].tolist()+data['shape'])))
# ------------------------------------------ output result -----------------------------------------

2
processing/post/addGradient.py
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@ -63,7 +63,7 @@ parser.add_option('-p','--pos','--periodiccellcenter',
dest = 'pos',
type = 'string', metavar = 'string',
help = 'label of coordinates [%default]')
parser.add_option('-d','--data',
parser.add_option('-l','--label',
dest = 'data',
action = 'extend', metavar = '<string LIST>',
help = 'label(s) of field values')