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DAMASK_EICMD
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changed to new structure (using damask module)

This commit is contained in:
Martin Diehl 2012-01-26 12:46:38 +00:00
parent 5a658d1b82
commit 27a976c04f
1 changed files with 73 additions and 157 deletions
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230
processing/post/addDivergence.py
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@ -23,29 +23,20 @@ class extendableOption(Option):
def location(idx,res): def location(idx,res):
return ( idx % res[0], \ return ( idx % res[0], \
(idx // res[0]) % res[1], \ ( idx // res[0]) % res[1], \
(idx // res[0] // res[1]) % res[2] ) ( idx // res[0] // res[1]) % res[2] )
def index(location,res): def index(location,res):
return ( location[0] % res[0] + \ return ( location[0] % res[0] + \
(location[1] % res[1]) * res[0] + \ ( location[1] % res[1]) * res[0] + \
(location[2] % res[2]) * res[0] * res[1] ) ( location[2] % res[2]) * res[1] * res[0] )
def prefixMultiply(what,len):
return {True: ['%i_%s'%(i+1,what) for i in range(len)],
False:[what]}[len>1]
# -------------------------------------------------------------------- # --------------------------------------------------------------------
# MAIN # MAIN
# -------------------------------------------------------------------- # --------------------------------------------------------------------
FDcoefficients = [ \
[1.0/2.0, 0.0, 0.0, 0.0],\
[2.0/3.0,-1.0/12.0, 0.0, 0.0],\
[3.0/4.0,-3.0/20.0,1.0/ 60.0, 0.0],\
[4.0/5.0,-1.0/ 5.0,4.0/105.0,-1.0/280.0],\
]
parser = OptionParser(option_class=extendableOption, usage='%prog options [file[s]]', description = """ parser = OptionParser(option_class=extendableOption, usage='%prog options [file[s]]', description = """
Add column(s) containing divergence of requested column(s). Add column(s) containing divergence of requested column(s).
Operates on periodic ordered three-dimensional data sets. Operates on periodic ordered three-dimensional data sets.
@ -59,8 +50,6 @@ parser.add_option('--fdm', dest='accuracy', action='extend', type='
help='degree of central difference accuracy') help='degree of central difference accuracy')
parser.add_option('--fft', dest='fft', action='store_true', \ parser.add_option('--fft', dest='fft', action='store_true', \
help='calculate divergence in Fourier space [%default]') help='calculate divergence in Fourier space [%default]')
parser.add_option('-m','--memory', dest='memory', action='store_true', \
help='memory efficient calculation (not possible for FFT based divergency [%default]')
parser.add_option('-v','--vector', dest='vector', action='extend', type='string', \ parser.add_option('-v','--vector', dest='vector', action='extend', type='string', \
help='heading of columns containing vector field values') help='heading of columns containing vector field values')
parser.add_option('-t','--tensor', dest='tensor', action='extend', type='string', \ parser.add_option('-t','--tensor', dest='tensor', action='extend', type='string', \
@ -69,16 +58,13 @@ parser.add_option('-d','--dimension', dest='dim', type='float', nargs=3, \
help='physical dimension of data set in x (fast) y z (slow) [%default]') help='physical dimension of data set in x (fast) y z (slow) [%default]')
parser.add_option('-r','--resolution', dest='res', type='int', nargs=3, \ parser.add_option('-r','--resolution', dest='res', type='int', nargs=3, \
help='resolution of data set in x (fast) y z (slow)') help='resolution of data set in x (fast) y z (slow)')
parser.add_option('-s','--skip', dest='skip', type='int', nargs=3, \
help='items skipped due to periodicity in x (fast) y z (slow)')
parser.set_defaults(accuracy = []) parser.set_defaults(accuracy = [])
parser.set_defaults(memory = False)
parser.set_defaults(fft = False) parser.set_defaults(fft = False)
parser.set_defaults(vector = []) parser.set_defaults(vector = [])
parser.set_defaults(tensor = []) parser.set_defaults(tensor = [])
parser.set_defaults(dim = []) parser.set_defaults(dim = [])
parser.set_defaults(skip = [0,0,0])
accuracyChoices = [2,4,6,8] accuracyChoices = [2,4,6,8]
(options,filenames) = parser.parse_args() (options,filenames) = parser.parse_args()
@ -89,12 +75,14 @@ if len(options.dim) < 3:
parser.error('improper dimension specification...') parser.error('improper dimension specification...')
if not options.res or len(options.res) < 3: if not options.res or len(options.res) < 3:
parser.error('improper resolution specification...') parser.error('improper resolution specification...')
for choice in options.accuracy: for choice in options.accuracy:
if int(choice) not in accuracyChoices: if int(choice) not in accuracyChoices:
parser.error('accuracy must be chosen from %s...'%(', '.join(accuracyChoices))) parser.error('accuracy must be chosen from %s...'%(', '.join(accuracyChoices)))
if options.fft: options.accuracy.append('fft') if options.fft: options.accuracy.append('FFT')
if not options.accuracy:
parser.error('no accuracy selected')
resSkip = map(lambda (a,b): a+b,zip(options.res,options.skip))
datainfo = { # list of requested labels per datatype datainfo = { # list of requested labels per datatype
'vector': {'len':3, 'vector': {'len':3,
'label':[]}, 'label':[]},
@ -109,174 +97,102 @@ if options.tensor != None: datainfo['tensor']['label'] += options.tensor
files = [] files = []
if filenames == []: if filenames == []:
files.append({'name':'STDIN', 'handle':sys.stdin}) files.append({'name':'STDIN', 'input':sys.stdin, 'output':sys.stdout})
else: else:
for name in filenames: for name in filenames:
if os.path.exists(name): if os.path.exists(name):
files.append({'name':name, 'handle':open(name)}) files.append({'name':name, 'input':open(name), 'output':open(name+'_tmp','w')})
# ------------------------------------------ loop over input files ---------------------------------------
# ------------------------------------------ loop over input files ---------------------------------------
for file in files: for file in files:
print file['name'] if file['name'] != 'STDIN': print file['name']
content = file['handle'].readlines() table = damask.ASCIItable(file['input'],file['output'],False) # make unbuffered ASCII_table
file['handle'].close() table.head_read() # read ASCII header info
table.info_append(string.replace('$Id$','\n','\\n') + \
# get labels by either read the first row, or - if keyword header is present - the last line of the header '\t' + ' '.join(sys.argv[1:]))
headerlines = 1
m = re.search('(\d+)\s*head', content[0].lower())
if m:
headerlines = int(m.group(1))
passOn = content[1:headerlines]
headers = content[headerlines].split()
data = content[headerlines+1:]
regexp = re.compile('1_\d+_')
for i,l in enumerate(headers):
if regexp.match(l):
headers[i] = l[2:]
active = {} active = {}
column = {} column = {}
values = {} values = {}
div_field ={} divergence = {}
head = [] head = []
for datatype,info in datainfo.items(): for datatype,info in datainfo.items():
for label in info['label']: for label in info['label']:
key = {True :'1_%s', key = {True :'1_%s',
False:'%s' }[info['len']>1]%label False:'%s' }[info['len']>1]%label
if key not in headers: if key not in table.labels:
print 'column %s not found...'%key sys.stderr.write('column %s not found...\n'%key)
else: else:
if datatype not in active: active[datatype] = [] if datatype not in active: active[datatype] = []
if datatype not in column: column[datatype] = {} if datatype not in column: column[datatype] = {}
if datatype not in values: values[datatype] = {} if datatype not in values: values[datatype] = {}
if datatype not in div_field: div_field[datatype] = {} if datatype not in divergence: divergence[datatype] = {}
if label not in divergence[datatype]: divergence[datatype][label] = {}
active[datatype].append(label) active[datatype].append(label)
column[datatype][label] = headers.index(key) column[datatype][label] = table.labels.index(key) # remember columns of requested data
values[datatype][label] = numpy.array([0.0 for i in xrange(datainfo[datatype]['len']*\ values[datatype][label] = numpy.array([0.0 for i in xrange(datainfo[datatype]['len']*\
options.res[0]*options.res[1]*options.res[2])]).\ options.res[0]*options.res[1]*options.res[2])]).\
reshape((options.res[0],options.res[1],options.res[2],\ reshape((options.res[0],options.res[1],options.res[2],\
3,datainfo[datatype]['len']//3)) datainfo[datatype]['len']//3,3))
for accuracy in options.accuracy:
divergence[datatype][label][accuracy] = numpy.array([0.0 for i in xrange(datainfo[datatype]['len']//3*\
options.res[0]*options.res[1]*options.res[2])]).\
reshape((options.res[0],options.res[1],options.res[2],\
datainfo[datatype]['len']//3))
table.labels_append(['%i_div%s(%s)'%(i+1,accuracy,label)
for i in xrange(datainfo[datatype]['len']//3)]) # extend ASCII header with new labels
for what in options.accuracy: # loop over all requested degrees of accuracy (plus potentially fft)
if not options.memory or what != 'fft': # FFT divergence excluded in memory saving mode
head += prefixMultiply('div%s(%s)'%(what,label),datainfo[datatype]['len']//3)
# ------------------------------------------ assemble header --------------------------------------- # ------------------------------------------ assemble header ---------------------------------------
output = '%i\theader'%(headerlines+1) + '\n' + \ table.head_write()
''.join(passOn) + \
string.replace('$Id$','\n','\\n')+ '\t' + \
' '.join(sys.argv[1:]) + '\n' + \
'\t'.join(headers + head) + '\n' # build extended header
# ------------------------------------------ read value field --------------------------------------- # ------------------------------------------ read value field ---------------------------------------
idx = 0 idx = 0
for line in data: while table.data_read(): # read next data line of ASCII table
items = line.split()[:len(headers)] (x,y,z) = location(idx,options.res) # figure out (x,y,z) position from line count
if len(items) < len(headers): # skip too short lines (probably comments or invalid)
continue
locSkip = location(idx,resSkip)
if ( locSkip[0] < options.res[0]
and locSkip[1] < options.res[1]
and locSkip[2] < options.res[2] ): # only take values that are not periodic images
for datatype,labels in active.items():
for label in labels:
values[datatype][label][locSkip[0]][locSkip[1]][locSkip[2]]\
= numpy.reshape(items[column[datatype][label]:
column[datatype][label]+datainfo[datatype]['len']],(3,datainfo[datatype]['len']//3))
idx += 1 idx += 1
# ------------------------------------------ read file --------------------------------------- for datatype,labels in active.items(): # loop over vector,tensor
if options.memory: for label in labels: # loop over all requested curls
idx = 0 values[datatype][label][x,y,z] = numpy.array(
for line in data: map(float,table.data[column[datatype][label]:
items = line.split()[:len(headers)] column[datatype][label]+datainfo[datatype]['len']]),'d').reshape(datainfo[datatype]['len']//3,3)
if len(items) < len(headers): # ------------------------------------------ process value field ---------------------------------------
continue
output += '\t'.join(items)
(x,y,z) = location(idx,options.res)
for datatype,labels in active.items():
for label in labels:
for accuracy in options.accuracy:
if accuracy == 'fft': continue
for k in range(datainfo[datatype]['len']/3): # formulas from Mikhail Itskov: Tensor Algebra and Tensor Analysis for Engineers, Springer 2009, p 52
theDiv = 0.0
for a in range(int(accuracy)//2):
theDiv += FDcoefficients[int(accuracy)//2-1][a] * \ for datatype,labels in active.items(): # loop over vector,tensor
( \ for label in labels: # loop over all requested divergencies
(values[datatype][label][location(index([x+1+a,y,z],options.res),options.res)[0]] \ for accuracy in options.accuracy:
[location(index([x+1+a,y,z],options.res),options.res)[1]] \ if accuracy == 'FFT':
[location(index([x+1+a,y,z],options.res),options.res)[2]][k][0] - \ divergence[datatype][label][accuracy] = damask.core.math.divergence_fft(options.res,options.dim,datainfo[datatype]['len']//3,values[datatype][label])
values[datatype][label][location(index([x-1-a,y,z],options.res),options.res)[0]] \ else:
[location(index([x-1-a,y,z],options.res),options.res)[1]] \ divergence[datatype][label][accuracy] = damask.core.math.divergence_fdm(options.res,options.dim,datainfo[datatype]['len']//3,eval(accuracy)//2-1,values[datatype][label])
[location(index([x-1-a,y,z],options.res),options.res)[2]][k][0]) * options.res[0] / options.dim[0] + \ # ------------------------------------------ process data ---------------------------------------
(values[datatype][label][location(index([x,y+1+a,z],options.res),options.res)[0]] \
[location(index([x,y+1+a,z],options.res),options.res)[1]] \ table.data_rewind()
[location(index([x,y+1+a,z],options.res),options.res)[2]][k][1] - \ idx = 0
values[datatype][label][location(index([x,y-1-a,z],options.res),options.res)[0]] \ while table.data_read(): # read next data line of ASCII table
[location(index([x,y-1-a,z],options.res),options.res)[1]] \ (x,y,z) = location(idx,options.res) # figure out (x,y,z) position from line count
[location(index([x,y-1-a,z],options.res),options.res)[2]][k][1]) * options.res[1] / options.dim[1] + \ idx += 1
(values[datatype][label][location(index([x,y,z+1+a],options.res),options.res)[0]] \
[location(index([x,y,z+1+a],options.res),options.res)[1]] \ for datatype,labels in active.items(): # loop over vector,tensor
[location(index([x,y,z+1+a],options.res),options.res)[2]][k][2]- \ for label in labels: # loop over all requested
values[datatype][label][location(index([x,y,z-1-a],options.res),options.res)[0]] \
[location(index([x,y,z-1-a],options.res),options.res)[1]] \
[location(index([x,y,z-1-a],options.res),options.res)[2]][k][2]) * options.res[2] / options.dim[2] \
)
output += '\t%f'%theDiv
output += '\n'
idx += 1
else:
for datatype,labels in active.items():
for label in labels:
if label not in div_field[datatype]: div_field[datatype][label] = {}
for accuracy in options.accuracy: for accuracy in options.accuracy:
div_field[datatype][label][accuracy] = numpy.array([0.0 for i in range((datainfo[datatype]['len'])//3*\ table.data_append(list(divergence[datatype][label][accuracy][x,y,z].reshape(datainfo[datatype]['len']//3)))
options.res[0]*options.res[1]*options.res[2])]).\
reshape((options.res[0],options.res[1],options.res[2],\ table.data_write() # output processed line
datainfo[datatype]['len']//3))
if accuracy == 'fft':
div_field[datatype][label][accuracy] = damask.core.math.divergence_fft(options.res,options.dim,datainfo[datatype]['len']//3,values[datatype][label])
else:
div_field[datatype][label][accuracy] = damask.core.math.divergence_fdm(options.res,options.dim,datainfo[datatype]['len']//3,eval(accuracy)//2-1,values[datatype][label])
idx = 0
for line in data:
items = line.split()[:len(headers)]
if len(items) < len(headers):
continue
output += '\t'.join(items)
for datatype,labels in active.items():
for label in labels:
for accuracy in options.accuracy:
for i in range(datainfo[datatype]['len']/3):
output += '\t%f'%div_field[datatype][label][accuracy][location(idx,options.res)[0]][location(idx,options.res)[1]][location(idx,options.res)[2]][i]
output += '\n'
idx += 1
# ------------------------------------------ output result --------------------------------------- # ------------------------------------------ output result ---------------------------------------
if file['name'] == 'STDIN': table.output_flush() # just in case of buffered ASCII table
print output
else: file['input'].close() # close input ASCII table
file['handle'] = open(file['name']+'_tmp','w') if file['name'] != 'STDIN':
try: file['output'].close # close output ASCII table
file['handle'].write(output) os.rename(file['name']+'_tmp',file['name']) # overwrite old one with tmp new
file['handle'].close()
os.rename(file['name']+'_tmp',file['name'])
except:
print 'error during writing',file['name']+'_tmp'